Human Computer Interaction can acquire several advantages with the introduction of different natural forms of device free communication. Gestures are a natural form of actions which we often use in our daily life for interaction, therefore to use it as a communication medium with computers generates a new paradigm of interaction with computers. In this paper, we propose to recognize dynamic hand gestures using Microsoft Kinect and control the VLC media player through those gestures. The user stands in front of Kinect sensor and performs appropriate gestures. The depth information is captured by Kinect. The depth image is used to segment the hand from the background using depth thresholding. The depth video is divided into frames and HoG feature extraction is applied to the extracted frames. To recognize the hand gestures using HoG features, we train a multi-class SVM classifier which will classify hand gestures. Finally, the VLC command corresponding to the predicted gesture will be executed.