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Solar energy estimation procedures are very important in the renewable energy field for development of mathematical models, optimization, and advanced control of processes. Solar radiation data provide information on how much of the sun's energy strikes a surface at a location on earth during a particular time period. These data are needed for effective research into solar-energy utilization. Due to the cost and difficulty in measurement, these data are not readily available. Therefore, there is the need to develop alternative ways of generating these data. In this study, an artificial neural network (ANN) was used for the estimation of daily global solar radiation (R-G) over the Norte Chico using 17 552 data measured from 21 meteorological stations (years 2004-2010) located in the south area of the Atacama Desert. The ANN was developed with particle swarm optimization. Six input parameters were used to train the network. These parameters were elevation, longitude, latitude, air temperature, relative humidity, and wind speed. The network that obtained the lowest deviation during the training was one with 6 neurons in the input layer, 18 and 6 neurons in the hidden layers, and one neuron in the output layer. The results show that the ANN can be accurately trained and that the chosen architecture can estimate the R-G with acceptable accuracy: average absolute relative deviation less than 10% for the training and for the validation step. The low deviations found with the proposed method indicate that it can estimate R-G with better accuracy than other methods available in the literature. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3699616]