Static gestures can convey certain meanings and act as specific transitions in dynamic gestures. Therefore, recognizing static gestures is one of the most important aspects of gesture recognition. In this paper, a new approach is presented for recognizing static gestures based on Zernike moments (ZMs) and pseudo-Zernike moments (PZMs). The binary hand silhouette is first accommodated with a minimum bounding circle (MBC). The binary hand silhouette is then decomposed into the finger part and the palm part by morphological operations according to the radius of the MBC. After that, the ZMs & PZMs of the finger part and the palm part with different importance, respectively, are computed based on the center of the MBC. Finally, 1-nearest neighbor techniques are used to perform feature matching between an input feature vector and stored feature vectors for static gesture identification. Results show that the proposed approach performs better than previous methods based on conventional ZMs & PZMs in recognizing static gestures. The proposed technique could be useful in improving the recognition rate of static gestures.