Two methods for modelling the performance of a desiccant wheel are presented: a physical model, based on mass and energy balances of the process, and a neural network model, based on the training of a black box model with real data. The physical model consists of a set of nonlinear differential equations solved by finite differences techniques. The neural network model consists of a four-input-four-output network that calculates the outlet conditions from inlet ones. Real data are used to validate the physical model and to train the neural network. The physical model shows discrepancies between calculated and measured values mainly due to the fact that the system is assumed to be adiabatic. The heat losses in the ducts and the wheel are not considered in the model, but in the experimental facility these losses occur. In the case of the neural network model, the temperature and humidity ratio calculated for the outlet air are in accordance with the experimental data.