Laparoscopic surgery is a challenging task in minimally invasive surgery, which involves complex instrument control, extensive manual dexterity, and hand-eye coordination. This requires a greater attention to training and skills evaluation. In order to provide a more objective skills assessment method, this paper presents a wireless sensor platform for the capture of laparoscopic hand gesture data and a hidden-Markov-model-based analysis framework for optimal sensor selection and placement. Detailed experimental validation is provided to illustrate how the proposed method can be used to assess surgical performance improvement over repeated training.