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This paper addresses the problem of identifying persons with a mobile robot. In the proposed system, people are first detected and then tracked with the robot's laser range-finder sensor, using an independent Kalman filter for each person. After segmentation, the rectangular region of the image containing the person is divided into regions corresponding to the person's head, torso and legs. Colour features are extracted from each region for input to a pattern recognition system. Five alternative classification methods were investigated, including experiments on a real robot and with a static camera system. The best identification performance was obtained with an ensemble of self-organizing maps (ESOM), where one self-organizing map is trained for each person in the robot's database. We also discuss how to incorporate the new method into a complete application of a robotic security guard.