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Abstract: Object: Nowadays, most of the surgical training programs follow a duration-based format that focuses on improving technical skills of trainees for a fixed amount of time before declaring their proficiency. More recently, different approaches have been proposed for the skills assessment; such as the objective structured clinical examination (OSCE). The OSCE consists of different stations in which trainees are required to perform practical exams while their performance is evaluated by examiners. However, their performance cannot be easily assessed by the simple observation of the task. As a result, no standard evaluation criteria can be conceived. Methods: Thanks to the recent advances in Robot Technology (RT); more efficient training systems can be conceived. In particular, authors believe in the importance of developing automated training devices designed to provide training progress quantitative information of trainees. For this reason, at Waseda University, since 2004, we have proposed as a long-term research goal, the development of a Patient Robot which nearly reproduces the human body anatomy and physiology by embedding sensors and actuators into a human model. Due to the complexity of patient robot development, as a first approach, we have proposed the development of a Suture/Ligature Training System. In this paper, the details of Waseda-Kyotokagaku Suture No. 2 Refined II (WKS-2RII) are presented. The WKS-2RII has been designed to reproduce the task conditions of the suture and ligature as well as to provide quantitative information of artificial skin movement, and the physical properties of the suture. From such collected data, we have proposed an Evaluation Function that integrates all the proposed evaluation parameters. Results: In order to verify the effectiveness of the WKS-2RII, a set of experiments were proposed to analyze the performance of subjects while performing the task with the WKS-2RII. The experiments were designed to determine if the proposed system may provide more detailed information of the task in a quantitative way. From the experimental results, we have confirmed that the WKS-2RII is capable of providing quantitative assessment of the task. In contrast to the conventional training methods (i.e., OSCE, etc.), the WKS-2RII can provide more detailed information of the task performance, so that the proposed system can detect the differences among different level of expertise (five surgeons, five medical students and five unskilled persons) as well as detect improvements of trainees by plotting the learning curve. Conclusions: In this paper, we have presented the improvements on the WKS-2RII and a unique evaluation function has been proposed. Regarding the weighting coefficients, the discriminant analysis method was used to determine the optimal values of the weighting coefficients.