Laparoscopic surgery poses many different constraints for the operating surgeon, resulting in a slow uptake of advanced laparoscopic procedures. Traditional approaches to the assessment of surgical performance rely on prior classification of a cohort of surgeons' technical skills for validation, which may introduce subjective bias to the outcome. In this study, Hidden Markov Models (HMMs) are used to learn surgical maneuvers from 11 subjects with mixed abilities. By using the leave-one-out method, the HMMs are trained without prior clustering of subjects into different skill levels, and the output likelihood indicates the similarity of a particular subject's motion trajectories to those of the group. The results show that after a short period of training, the novices become more similar to the group when compared to the initial pre-training assessment. The study demonstrates the strength of the proposed method in ranking the quality of trajectories of the subjects, highlighting its value in minimizing the subjective bias in skills assessment for minimally invasive surgery.