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In this paper, we propose a method for designing an identification system of human-robot contact states based on tactile recognition. First, a method of quantifying tactile cognition of a human (receiver) touched by other people (toucher) using a neural network called MCP (modified counter propagation) is presented, which matches the verbal response by the receiver with tactile stimulation detected during physical interference and contact utilizing tactile interface. It is incorporated that the probability of corresponding contact state is determined, based on the degree of similarity of the characteristics between new input data and reference data patterns stored in advance. Referring to the SOM (self-organizing maps) formed through learning, which contains the relationship between contact states and tactile stimulation detected, a robot that comes into contact with a human can recognize and infer contact states from tactile stimulation like the receiver. Next, in order to accomplish high-performance of contact state identification by improving the learning performance, an evaluation criterion to quantify the discriminatability of contact states is proposed. Finally, the experimental results confirm that the proposed method is useful for identifying contact states, based on only tactile sensing, as represented by the receiver.