A method was introduced for hand posture recognition based on compressive sensing.Considering the limitations of a single feature, Zernike moment and HOG descriptors were fused to improve the robustness.Firstly, we constructed training dictionaries according to the characteristics, then the candidate target was expressed as a sparse combination of the corresponding training dictionary, and classification results were done through solving a l1-norm based optimization problem.The proposed method can take full advantage of each feature, which is robust to rotation, noise and varying illumination.Experiment results show that the algorithm is competitive to the state-of-the-art hand posture recognition methods, and is suitable for real-time application.