The design of thermal systems usually requires the prediction of heat transfer rates of heat exchangers under prescribed operating conditions. Due to the complexity of these thermal components, conventional steady-state modeling approaches, such as correlations, provide predictions with large uncertainties. These are not only due to experimental errors but also to the information compression process in which several assumptions are used. For control purposes, furthermore, dynamic simulations are needed for which only a limited number of models are available. We apply artificial neural networks (ANNs) to the simulation of the steady and dynamic behaviors of heat exchangers, as well as to the control of fluid temperatures. The experiments were carried out in a heat exchanger test facility. The ANN predictions are obtained using information about the flow rates and inlet temperatures of both fluids in the heat exchanger. Numerical tests show the feasibility of the method and experimental comparison with conventional correlations prove the ANN to be more accurate.