This paper describes an application of artificiai neural networks (ANNs) to the prediction of Iocai Iosses from integrated emitters. First, the optimum input-output combination was determined. Then, the mapping capability of ANNs and regression models was compared. Afterwards, a five-input ANN model, which considers pipe and emitter internai diameter, emitter length, emitter spacing, and pipe discharge, was used to develop a local losses predicting tool which was obtained from different training strategies while taking into account a completely independent test set. Finally, a performance index was evaluated for tbe test emitter models studied. Emitter data with Iow reliability were removed from the processo Performance indexes over 80% were obtained for the remaining test emitters.