A subunit-based Dynamic Time Warping (DTW) approach is proposed for hand movement recognition. Two major contributions distinguish the proposed approach from conventional DTW. (1) A set of hand movement subunits is constructed using a data-driven method. The common sub-movements (subunits) are shared across hand gestures to obtain a smaller training data size and search space to improve recognition performance. (2) A similarity measure robust to variability is offered using subunit-to-subunit matching to absorb the difference between two similar sub-sequences belonging to the same subunit, and only keeping the distances between sub-sequences that relate to different subunits. Our experimental results demonstrate the efficiency and accuracy of the proposed approach.