A multi-layer neural network is used to control the navigation of a mobile robot in an environment containing obstacles in a temperature field. The mobile robot must avoid obstacles and hill climb towards the maximum of the temperature field. The strategy for each of these two tasks is acquired by learning. First by exploring the environment, the mobile robot extracts the relevant sensory situations by building up an internal map of the environment. The associations between these situations and the appropriate actions are then formed in an unsupervised manner, i.e. with no ‘teacher’required. The proposed structure of the system permits the coordination of the two tasks. Simulation results display not only the ability of the robot to achieve collision-free navigation towards its target in the explored environment, but also in new unvisited environments, illustrating the generalization property of neural networks.