In the present study we apply an artificial neural network to predict the operation of a humid air-water fin-tube compact heat exchanger. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Published experimental data, corresponding to humid air flowing over the heat exchanger tubes and water flowing inside them, are used to train the neural network. After training with known experimental values of the humid-air flow rates, dry-bulb and wet-bulb inlet temperatures for various geometrical configurations, the j-factor and heat transfer rate predictions of the network were tested against the experimental values. Comparisons were made with published predictions of power-law correlations which were obtained from the same data. The results demonstrate that the neural network is able to predict the performance of this heat exchanger much better than the correlations.