The use of hand gesture provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). Many methods for hand gesture recognition using visual analysis have been proposed such as syntactical analysis, neural network(NN), Hidden Markov Model(HMM) and so on. In our research, a HMM is proposed for alphabetical hand gesture recognition.In the preprocessing stage, the proposed approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects the candidate regions on the basis of skin-color and motion in an image.. The hand tracking algorithm finds the centroid of a moving hand region, connect those centroids, and thus, produces a trajectory. The spotting algorithm divides the trajectory into real and meaningless gestures.In constructing a feature database, the proposed approach use the location, angle and velocity feature code, and employ a k-means algorithm for code-book of HMM. In our experiments, 2400 trained gestures and 2400 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a higher and satisfying recognition rate with various gestures.