In this paper we present a self-learning method for low-level navigation for autonomous mobile robots, based on a neural network. Both corridor following and obstacle avoidance in indoor environments are successfully managed by the same network. Raw gray scale images of size 32 by 23 pixels are processed one by one by a feed forward neural network. The output signals of the network represent the appropriate steering actions of the robot.