Human behavior Analysis, using visual information in a given image or sequence of images, has been an active area of research in computer vision community. The image captured by conventional camera does not provide the suitable information to perform comprehensive analysis. However, depth sensors have recently made a new type of data available. Most of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions based on depth images. In this paper, an efficient method for human action recognition is proposed. Our research makes the following contributions: the proposed method makes an efficient representation of human actions by constructing a feature vector based on the humans skeletal information extracted from depth images. Then, introducing these feature vectors to Multi-class Support Vector Machine (MSVM) to perform the action classification task. The proposed representation of the human action ensures it is invariant to the scale of the subjects/objects and the orientation to the camera, while it maintains the correlation among different body parts. A number of experiments have been performed in order to evaluate the proposed algorithm. The results revealed that the proposed algorithm is efficient and leads to an improved action recognition process. Moreover, it is suitable for implementation in a real-time behavior analysis