In this paper, we present a novel feature extraction approach based on Curvature Scale Space (CSS) for translation, scale, and rotation invariant recognition of hand poses. First, the CSS images are used to represent the shapes of boundary contours of hand poses. Then, we extract the multiple sets of CSS features to overcome the problem of deep concavities in contours of hand poses. Finally, nearest neighbour techniques are used to perform CSS matching between the multiple sets of input CSS features and the stored CSS features for hand pose identification. Results show the proposed approach can extract the multiple sets of CSS features from the input images and perform well for recognition of hand poses.