This work used artificial neural network(ANN)to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles,based on limited experimental data.The BackPropagation (BP) algorithm was used in training the networks.Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2%.Comparison withcorrelation for prediction shows ANN superiority.It is recommended that ANN can be easily used to predict theperformances of thermal systems in engineering applications,especially to model heat exchangers for heattransfer analysis. This work used artificial neural network (ANN) to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles, based on limited experimental data. The Back Propagation (BP) algorithm was used in training the networks. Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2% .Comparison withcorrelation for prediction shows ANN superiority. It is recommended that ANN can be easily used to predict the performance of of thermal systems in engineering applications, especially to model heat exchangers for heattransfer analysis.