Surgical movement is composed of discrete gestures that are combined to perform complex surgical procedures. A promising approach to objective surgical skill evaluation systems is kinematics and kinetic analysis of hand movement that yields a gesture level analysis of proficiency of a performed movement. In this paper, we propose a novel system that combines surgical gesture segmentation, surgical gesture recognition, and expertise analysis of surgical profiles in minimally invasive surgery (MIS). Kinematic analysis was used to segment gestures from a continuous motion stream. Human anatomy driven Hidden Markov Models (HMMs) are adopted for gesture recognition and expertise identification. When the proposed system was tested on a library of 200 samples for every basic surgical gesture, the gesture recognition module reported a perfect accuracy rate for the basic gestures, while the expertise identification module showed 94.7% accuracy.