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diff --git a/src/main/java/org/apache/commons/math3/stat/descriptive/moment/VectorialCovariance.java b/src/main/java/org/apache/commons/math3/stat/descriptive/moment/VectorialCovariance.java
new file mode 100644
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@@ -0,0 +1,157 @@
+/*
+ * 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.stat.descriptive.moment;
+
+import java.io.Serializable;
+import java.util.Arrays;
+
+import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.linear.MatrixUtils;
+import org.apache.commons.math3.linear.RealMatrix;
+
+/**
+ * Returns the covariance matrix of the available vectors.
+ * @since 1.2
+ */
+public class VectorialCovariance implements Serializable {
+
+    /** Serializable version identifier */
+    private static final long serialVersionUID = 4118372414238930270L;
+
+    /** Sums for each component. */
+    private final double[] sums;
+
+    /** Sums of products for each component. */
+    private final double[] productsSums;
+
+    /** Indicator for bias correction. */
+    private final boolean isBiasCorrected;
+
+    /** Number of vectors in the sample. */
+    private long n;
+
+    /** Constructs a VectorialCovariance.
+     * @param dimension vectors dimension
+     * @param isBiasCorrected if true, computed the unbiased sample covariance,
+     * otherwise computes the biased population covariance
+     */
+    public VectorialCovariance(int dimension, boolean isBiasCorrected) {
+        sums         = new double[dimension];
+        productsSums = new double[dimension * (dimension + 1) / 2];
+        n            = 0;
+        this.isBiasCorrected = isBiasCorrected;
+    }
+
+    /**
+     * Add a new vector to the sample.
+     * @param v vector to add
+     * @throws DimensionMismatchException if the vector does not have the right dimension
+     */
+    public void increment(double[] v) throws DimensionMismatchException {
+        if (v.length != sums.length) {
+            throw new DimensionMismatchException(v.length, sums.length);
+        }
+        int k = 0;
+        for (int i = 0; i < v.length; ++i) {
+            sums[i] += v[i];
+            for (int j = 0; j <= i; ++j) {
+                productsSums[k++] += v[i] * v[j];
+            }
+        }
+        n++;
+    }
+
+    /**
+     * Get the covariance matrix.
+     * @return covariance matrix
+     */
+    public RealMatrix getResult() {
+
+        int dimension = sums.length;
+        RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);
+
+        if (n > 1) {
+            double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
+            int k = 0;
+            for (int i = 0; i < dimension; ++i) {
+                for (int j = 0; j <= i; ++j) {
+                    double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
+                    result.setEntry(i, j, e);
+                    result.setEntry(j, i, e);
+                }
+            }
+        }
+
+        return result;
+
+    }
+
+    /**
+     * Get the number of vectors in the sample.
+     * @return number of vectors in the sample
+     */
+    public long getN() {
+        return n;
+    }
+
+    /**
+     * Clears the internal state of the Statistic
+     */
+    public void clear() {
+        n = 0;
+        Arrays.fill(sums, 0.0);
+        Arrays.fill(productsSums, 0.0);
+    }
+
+    /** {@inheritDoc} */
+    @Override
+    public int hashCode() {
+        final int prime = 31;
+        int result = 1;
+        result = prime * result + (isBiasCorrected ? 1231 : 1237);
+        result = prime * result + (int) (n ^ (n >>> 32));
+        result = prime * result + Arrays.hashCode(productsSums);
+        result = prime * result + Arrays.hashCode(sums);
+        return result;
+    }
+
+    /** {@inheritDoc} */
+    @Override
+    public boolean equals(Object obj) {
+        if (this == obj) {
+            return true;
+        }
+        if (!(obj instanceof VectorialCovariance)) {
+            return false;
+        }
+        VectorialCovariance other = (VectorialCovariance) obj;
+        if (isBiasCorrected != other.isBiasCorrected) {
+            return false;
+        }
+        if (n != other.n) {
+            return false;
+        }
+        if (!Arrays.equals(productsSums, other.productsSums)) {
+            return false;
+        }
+        if (!Arrays.equals(sums, other.sums)) {
+            return false;
+        }
+        return true;
+    }
+
+}