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 for generating these data. In this study, an artificial neural network (ANN) was used for the estimation of daily global solar radiation (5-min R-G) on tilted surface using data measured from the meteorological station located inside the University of Blida. Six input parameters were used to train the network. These parameters were elevation, longitude, latitude, air temperature, relative humidity, and wind speed. The optimized network obtained with lowest deviation during the training was one with 6 neurons in the input layer; neurons in the hidden were obtained by trial and error, 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 5-min R-G with acceptable accuracy: mean absolute error (MAE) less than 20% for both training and 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.