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We present an algorithm for Dynamic Time Warping (DTW) on multi-dimensional time series (MDDTW). The algorithm utilises all dimensions to find the best synchronisation. It is compared to ordinary DTW, where a single dimension is used for aligning the series. Both one-dimensional and multidimensional DTW are also tested when derivatives instead of feature values are used for calculating the warp. MD-DTW performed best in finding a known ground truth under noisy conditions. The algorithms were also used to perform simple classification of a set of 121 gestures. MD-DTW performed as well as or better than any single dimension in all tasks. In general, DTW on feature derivatives gave better results than DTW on feature values.