Empirical models designed to simulate and predict urban land use change are generally based on the utilisation of statistical techniques to reckon the land use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non-parametric approaches. This work introduces a simulation experiment on urban land use change in which a supervised back-propagation neural network has been employed in the parameterisation of the simulation model. The thereof estimated spatial land use transition probabilities feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium-sized town in the midwest of São Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985-1999 were produced, and statistical validation tests were then conducted for the best results, upon basis of a multiple resolution fitting procedure.