In this paper, we have proposed a new representation for human gait recognition which is called as mass vector. The mass vector along a given row is defined as the number of pixels with a nonzero value in a given row of the binarized silhouette of a walking person. Sequences of temporally ordered mass vector are used to represent a gait of an individual. Besides, different gait features are extracted from the mass vector such as the down-sampled mass vectors and the principal components of mass vectors. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occurring changes in walking speed. Experimental results show that mass vector has a higher discriminative power than previous works for gait recognition.