Explanation-based neural network learning (EBNN) has recently been introduced as a method for reducing the amount of training data required for reliable generalization, by relying instead on approximate, previously learned knowledge. We present first experiments applying EBNN to the problem of learning object recognition for a mobile robot. In these experiments, a mobile robot traveling down a hallway corridor learns to recognize distant doors based on color camera images and sonar sensations. The previously learned knowledge corresponds to a neural network that recognizes nearby doors, and a second network that predicts the state of the world after travelling forward in the corridor. Experimental results show that EBNN is able to use this approximate prior knowledge to significantly reduce the number of training examples required to learn to recognize distant doors. We also present results of experiments in which networks learned by EBNN (e.g., ``there is a door 2 meters ahead'') are then used as background knowledge for learning subsequent functions (e.g., ``there is a door 3 meters ahead'').