We demonstrate a novel application of nonlinear systems in the design of pattern classification systems. We show that pattern classification systems can be designed based upon training algorithms designed to control the qualitative behaviour of a nonlinear system. Our paradigm is illustrated by means of a simple chaotic system-the Baker's map. Algorithms for training the system are presented and examples are given to illustrate the operation and learning of the system for pattern classification tasks. Â© 2003 Elsevier B.V. All rights reserved.