Artificial immune systems (AIS) constitute an emerging and promising field, and have been applied to pattern recognition and classification tasks to a limited extent so far. This work is a first attempt of applying the clonal selection principle to the training of multi-layer perceptrons (MLPs). The clonal selection based neural classifier (CSNC) uses the basic concepts of clonal selection to evolve MLPs, which are represented as real-valued linear antibodies. The proposed system is actually a multi-classifier, consisting of multiple sets of MLPs, each one devoted to the recognition of a different class of the input data. The final trained classifier is comprised of the best MLPs from each set. The proposed classifier is tested against a set of benchmark problems and yields promising results.