This paper presents a mobile control system capable of learn behaviors based on human examples. Our approach is based on image processing, template matching, finite state machine, and template memory. The system proposed allows image segmentation using neural networks in order to identify navigable and non-navigable regions. It also uses supervised learning techniques which work with different levels of memory of the templates. As output our system is capable controlling speed and steering for autonomous mobile robot navigation. Experimental tests have been carried out to evaluate the learning techniques.