In this study, the effect of relative humidity on solar potential is investigated using artificial neural-networks. Two different models are used to train the neural networks. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine-duration, and mean temperature) are used in the input layer of the network (Model 1). But, relative humidity values are added to one network in model (Model 2). In other words, the only difference between the models is relative humidity. New formulae based on meteorological and geographical data, have been developed to determine the solar energy potential in Turkey using the networks' weights for both models. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was obtained by the SCG algorithm with nine neurons for both models. Meteorological data for the four years, 2000-2003, for 18 cities (Artvin, Cesme, Bozkurt, Malkara, Florya, Tosya, Kizilcahamam, Yenisehir, Edremit, Gediz, Kangal, Solhan, Ergani, Selquk, Milas, Seydisehir, Siverek and Kilis) spread over Turkey have been used as data in order to train the neural network. Solar radiation is in output layer. One month for each city was used as test data, and these months have not been used for training. The maximum mean absolute percentage errors (MAPEs) for Tosya are 2.770394% and 2.8597% for Models 1 and 2, respectively. The minimum MAPEs for Seydiehir are 1.055205% and 1.041% with R-2 (99.9862%, 99.9842%) for Models 1 and 2, respectively, in the SCG algorithm with nine neurons. The best value of R 2 for Models 1 and 2 are for Seydiehir. The minimum value of R-2 for Model 1 is 99.8855% for Tosya, and the value for Model 2 is 99.9001% for Yenisehir. Results show that the humidity has only a negligible effect upon the prediction of solar potential using artificial neural-networks. (c) 2004 Elsevier Ltd. All rights reserved.