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In the human action recognition area, so far 2D action recognition has been studied extensively. Recently some studies, understanding human actions in 3D is emerging due to development of devices collecting 3D data. In this thesis, a new human behavior recognition method, that we call silhouette flows, is proposed for 3D data sequences of depth map. The method proposed in this thesis constitutes two steps, which are the feature extraction and classification. In feature extraction part, motion features are extracted from the 3D binary depth data in order to discern possibilities for action within the environment. For this purpose, the 3D depth data is projected on to cartesian planes in order to obtain silhouettes in frontal, top and side views and then optical flow vector fields on these planes over each frame of the video are computed. After finding these flow vectors, averages are prepared according to the motion vector values separately for negative and positive values for each frame of each plane. In order to recognize various human behaviors, each frame in video is divided into some meaningful blocks. According to the significant motion blocks, the final motion feature vector is obtained. Then, this motion feature vector is given to the SVM classification system and the results are investigated. All experiments are conducted on depth map data “MSR Action3D Dataset”. This dataset includes twenty human actions depth map sequences recorded with Microsoft Kinect depth sensors for ten different people. The experimental results are quite successful and the proposed method outperformed in some test the other methods existing in literature for the same data.M.S. - Master of Scienc