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In order to improve the accuracy of human abnormal behavior recognition, an action recognition algorithm using multiple features is employed in this paper, actions mainly including walking, jogging, running, boxing, hand waving, hand clapping. Firstly, human silhouette is extracted from video flowing. Then, Hu-moment features and texture features are extracted from this silhouette. Finally, the similarity between current behavior feature vectors and feature vectors of standard template is calculated using Mahalanobis distance. Experiment results show that this method has a higher recognition rate than that which extracts unique feature and it is of a great practical value.