Classification of hand gesture is crucial for the development of hand gesture based system for human machine interaction. Gesture recognition system consists of hand gesture acquisition, segmentation, morphological filtering, contour extraction, and classification. This paper aims at classification of hand gesture as a similarity measure using Dynamic Time Warping and Piecewise Dynamic Time Warping. Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified. Additionally, it is also estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW. © 2014 IEEE.