Abstract: This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ETo) and compares the performance of ANNs with the conventional method (Penman–Monteith) used to estimate ETo. Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. The input parameters and the ANN model architecture was decided based on use of MARS tool and trial and error approach leading to optimal error statistics. Different ANN architectures namely BPNN, RBFNN and GRNN were used. Model performance show that BPNN architecture suits for prediction of reference crop evapotranspiration and can be used for future scenario in the Ameleke watershed.