A method of supervised learning is described which improves fault tolerance by means of modifying the learning algorithm in order to introduce significant information related to fault tolerance during training. The method exploits the evolutive nature of the learning algorithm of radial basis function networks and employs optimization techniques to control the balance between generalization performance and fault tolerance. The technique developed is specific to the neural architecture employed, though it cat be used concurrently with other more traditional approaches like training with faults or retraining. The fault-tolerant algorithm presented provides a simple and efficient means of improving fault tolerance, and this is illustrated using examples taken from two different classification problems.