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diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/HitHistogram.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/HitHistogram.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math3.ml.neuralnet.twod.util;
+
+import org.apache.commons.math3.ml.neuralnet.MapUtils;
+import org.apache.commons.math3.ml.neuralnet.Neuron;
+import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
+import org.apache.commons.math3.ml.distance.DistanceMeasure;
+
+/**
+ * Computes the hit histogram.
+ * Each bin will contain the number of data for which the corresponding
+ * neuron is the best matching unit.
+ * @since 3.6
+ */
+public class HitHistogram implements MapDataVisualization {
+    /** Distance. */
+    private final DistanceMeasure distance;
+    /** Whether to compute relative bin counts. */
+    private final boolean normalizeCount;
+
+    /**
+     * @param normalizeCount Whether to compute relative bin counts.
+     * If {@code true}, the data count in each bin will be divided by the total
+     * number of samples.
+     * @param distance Distance.
+     */
+    public HitHistogram(boolean normalizeCount,
+                        DistanceMeasure distance) {
+        this.normalizeCount = normalizeCount;
+        this.distance = distance;
+    }
+
+    /** {@inheritDoc} */
+    public double[][] computeImage(NeuronSquareMesh2D map,
+                                   Iterable<double[]> data) {
+        final int nR = map.getNumberOfRows();
+        final int nC = map.getNumberOfColumns();
+
+        final LocationFinder finder = new LocationFinder(map);
+
+        // Total number of samples.
+        int numSamples = 0;
+        // Hit bins.
+        final double[][] hit = new double[nR][nC];
+
+        for (double[] sample : data) {
+            final Neuron best = MapUtils.findBest(sample, map, distance);
+
+            final LocationFinder.Location loc = finder.getLocation(best);
+            final int row = loc.getRow();
+            final int col = loc.getColumn();
+            hit[row][col] += 1;
+
+            ++numSamples;
+        }
+
+        if (normalizeCount) {
+            for (int r = 0; r < nR; r++) {
+                for (int c = 0; c < nC; c++) {
+                    hit[r][c] /= numSamples;
+                }
+            }
+        }
+
+        return hit;
+    }
+}