In this paper, a framework for human hand movement recognition is implemented for humans to play rock-paper-scissors game interactively against computer using a webcam. The game is the classic rock-paper-scissors game; however, first player is human and second player is computer. Human hand movements are recognized using a webcam, and the game is fully controlled using hand gestures. A dataset is constructed in this work by collecting hand images of different people. The dataset contains rock, paper, and scissors movements of different humanbeings. There are 8 photos for each movement and person using their left and right hands. The dataset is constructed using a controlled environment with black background and close up hand images. To recognize player movements two different feature extraction methods and two different classification methods are used and compared. Histogram of oriented gradients feature extraction method combined with support vector machines are found to be effective. It presents fast and effective computer vision framework for seamless playing experience with a sensitivity of % 92.9.