diff --git a/docs/more/whatsnew.rst b/docs/more/whatsnew.rst
index 3e27a5855..a3209013d 100644
--- a/docs/more/whatsnew.rst
+++ b/docs/more/whatsnew.rst
@@ -41,6 +41,10 @@ Wildboar 1.2 introduces several new models.
     the specified top `k` dimensions with the highest score.
   - :class:`~wildboar.dimension_selection.SelectDimensionPercentile`: only retain
     the dimensions with a `p-value` below the specified alpha level.
+  - :class:`~wildboar.dimension_selection.ECSSelector`: Select time series
+    dimensions based on the sum of distances between pairs of classes.
+
+
 
 - Add a new module :mod:`~wildboar.distance.lb` for distance lower bounds. The module
   contains four lower bounds.
diff --git a/src/wildboar/dimension_selection/__init__.py b/src/wildboar/dimension_selection/__init__.py
index 5ba377836..7d66d40b1 100644
--- a/src/wildboar/dimension_selection/__init__.py
+++ b/src/wildboar/dimension_selection/__init__.py
@@ -7,6 +7,7 @@
     SelectDimensionSignificance,
     SelectDimensionTopK,
 )
+from ._ecp import ECSSelector
 from ._sequential import SequentialDimensionSelector
 from ._variance import DistanceVarianceThreshold
 
@@ -16,4 +17,5 @@
     "SelectDimensionPercentile",
     "SelectDimensionSignificance",
     "SelectDimensionTopK",
+    "ECSSelector",
 ]
diff --git a/src/wildboar/dimension_selection/_ecp.py b/src/wildboar/dimension_selection/_ecp.py
new file mode 100644
index 000000000..d46f7c885
--- /dev/null
+++ b/src/wildboar/dimension_selection/_ecp.py
@@ -0,0 +1,140 @@
+# Authors: Isak Samsten
+# License: BSD 3 clause
+
+import abc
+import numbers
+
+import numpy as np
+from sklearn.base import _fit_context
+from sklearn.validation._parameter_constraints import Interval, StrOptions
+
+from ..base import BaseEstimator
+from ..distance._distance import pairwise_distance
+from ..distance.dtw import dtw_average
+from ..utils.validation import MetricOptions, check_classification_targets
+from ._base import DimensionSelectorMixin
+
+
+class _Prototype(metaclass=abc.ABCMeta):
+    def __init__(self, *args, **kwargs):
+        pass
+
+    @abc.abstractmethod
+    def create(self, X):
+        pass
+
+
+class _DtwPrototype(_Prototype):
+    def __init__(self, r):
+        self.r = r
+
+    def create(self, X):
+        return dtw_average(X, r=self.r)
+
+
+class _MeanPrototype(_Prototype):
+    def create(self, X):
+        return np.mean(X, axis=0)
+
+
+class _MedianPrototype(_Prototype):
+    def create(self, X):
+        return np.median(X, axis=0)
+
+
+_PROTOTYPE = {"mean": _MeanPrototype, "median": _MeanPrototype, "dtw": _DtwPrototype}
+
+
+class ECSSelector(DimensionSelectorMixin, BaseEstimator):
+    """
+    ElboxClassSum (ECS) dimension selector.
+
+    Select time series dimensions based on the sum of distances between pairs
+    of classes.
+
+    Parameters
+    ----------
+    prototype : {"mean", "median", "dtw"}, optional
+        The method for computing the class prototypes.
+    r : float, optional
+        The warping width if `prototype` is "dtw".
+    metric : str, optional
+        The distance metric.
+    metric_params : dict, optional
+        Optional parameters to the distance metric.
+
+        Read more about the metrics and their parameters in the
+        :ref:`User guide <list_of_metrics>`.
+    """
+
+    _parameter_constraints = {
+        "prototype": [StrOptions(_PROTOTYPE.keys())],
+        "r": [
+            None,
+            Interval(numbers.Integral, 0, None, closed="left"),
+            Interval(numbers.Real, 0, 1, closed="both"),
+        ],
+        "metric": [MetricOptions()],
+        "metric_params": [None, dict],
+    }
+
+    def __init__(
+        self, prototype="mean", r=None, metric="euclidean", metric_params=None
+    ):
+        self.prototype = prototype
+        self.metric = metric
+        self.metric_params = metric_params
+        self.r = r
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y):
+        """
+        Learn the dimensions to select.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_dims, n_timestep)
+            The training samples.
+        y : array-like of shape (n_samples, )
+            Ignored.
+
+        Returns
+        -------
+        object
+            The instance itself.
+        """
+        X, y = self._validate_data(X, y, allow_3d=True, ensure_min_dims=2)
+        check_classification_targets(y)
+        labels = np.unique(y)
+        n_labels = len(labels)
+        n_combinations = int(n_labels * (n_labels - 1) / 2)
+        _, n_dims, n_timestep = X.shape
+
+        prototype_factory = _PROTOTYPE[self.prototype](
+            self.r if self.r is not None else 1.0
+        )
+        prototypes = np.empty((n_dims, n_labels, n_timestep), dtype=X.dtype)
+        for i in range(n_labels):
+            for d in range(n_dims):
+                prototypes[d, i] = prototype_factory.create(X[y == labels[i], d])
+
+        proto_dist = np.empty((n_dims, n_combinations), dtype=float)
+        for d in range(n_dims):
+            k = 0
+            for i in range(n_labels):
+                for j in range(i + 1, n_labels):
+                    proto_dist[d, k] = pairwise_distance(
+                        prototypes[d, i], prototypes[d, j], metric=self.metric
+                    )
+                    k += 1
+
+        dim_sum = np.sum(proto_dist, axis=1)
+        self.dim_sort_ind_ = np.argsort(dim_sum)[::-1]
+        p = np.linspace(dim_sum.max(), dim_sum.min(), dim_sum.size)
+        self.elbow_ = np.abs(dim_sum[self.dim_sort_ind_] - p).argmax()
+        return self
+
+    def _get_dimensions(self):
+        mask = np.zeros(self.n_dims_in_, dtype=bool)
+        mask[self.dim_sort_ind_[: self.elbow_]] = True
+        return mask