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Giving an advice to a mobile robot still requires classical user interfaces. A more intuitive way of commanding can be provided by verbal or gesture commands. In this article, we present new approaches and enhancements for established methods that are in use in our laboratory. Our aim is to direct a robot with simple dynamic gestures. We focus on visual gesture recognition. Based on skin color segmentation algorithms for tracking the user's hand, hidden Markov models are used for gesture type recognition. The filters applied to the recorded trajectory strongly compress the input data. They also mark start and end point of a possible gesture. The hidden Markov models have been enhanced by a threshold model in order to wipe out insignificant movements. Pre-classification of the reference gestures serves for keeping computational effort low.