In the task of human action recognition in uncontrolled video, motion features are used widely in order to achieve subject and appearence invariance. We implemented 3 Histograms of Oriented Optical Flow based method which have a common motion feature extraction phase. We compute an optical flow field over each frame of the video. Then those flow vectors are histogrammed due to angle values to represent each frame with a histogram. In order to capture local motions, The bounding box of the subject is divided into grids and the angle histograms of all grids are concetanated to obtain the final motion feature vector. Motion Features are supplied to 3 dierent classification system alternatives containing clustering combined with HMM, clustering with K-nearest neighbours and average histograms methods. Three methods are implemented and results are evaluated over Weizmann and KTH datasets.