We describe a methodology for classification of gait (walk, run, jog, etc.) and recognition of individuals based on gait using two successive stages of principal component analysis (PCA) on kinematic data. In psychophysical studies we have found that observers are sensitive to specific "motion features" that characterize human gait. These spatiotemporal motion features closely correspond to the first few principal components (PC) of the kinematic data. The first few PCs provide a representation of an individual gait as trajectory along a low-dimensional manifold in PC space. A second stage of PCA captures variability in the shape of this manifold across individuals or gaits. This simple eigenspace based analysis is capable of accurate classification across subjects.