Human action recognition (HAR) has become one of the most active topi cs in computer vision and pattern recognition due to a wide range of promising applications.In order to o vercome the deficiency of single representation method,a new recognition a lgorithm of human action based on multi-feature fusion and support vector machine (SVM) is presented in this p aper.The proposed algorithm consists of three essential cascade modules.First,the human silhouette is obta ined by separating the salient regions and the background based on background subtraction.Then,the multi-feature fusio n is constructed by using two types of available features,the histogram of the silhouette and the optic flow. The human activity recognition can commonly be viewed as a multiclass classification problem.Final ly,the multiple features are sent to the SVM for recognizing the human activity.The experimental results sho w that the proposed method can achieve the correct recognition rate above 99.8% for the Weizmann benchmark data set.Inter-related analyses conclude that the proposed algorithm is effective and promising.The recogni tion performance of the SVM classifiers and some other mainstream classification techniques is also com pared,which further verifies the effectiveness of the proposed algorithm.