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Recent advances in depth sensing provide exciting opportunities for the development of new methods for human activity understanding. Yet, little work has been done in the area of hand gesture recognition which has many practical applications. In this paper we propose a real-time system for dynamic hand gesture recognition. It is fully automatic and robust to variations in speed and style as well as in hand orientations. Our approach is based on action graph, which shares similar robust properties with standard HMM but requires less training data by allowing states shared among different gestures. To deal with hand orientations, we have developed a new technique for hand segmentation and orientation normalization. The proposed system is evaluated on a challenging dataset of twelve dynamic American Sign Language (ASL) gestures.