The knowledge of net radiation at the surface is of fundamental importance because it defines the total amount of energy available for the physical and biological processes such as evapotranspiration, air and soil warming. It is measured with net radiometers, but, the radiometers are expensive sensors, difficult to handle, that require constant care and also involve periodic calibration. This paper presents a methodology based on neural networks in order to replace the use of net radiometers (expensive tools) by modeling the relationships between the net radiation and meteorological variables measured in meteorological stations. Two different data sets (acquired at different locations) have been used in order to train and validate the developed artificial neural model. The statistical results (low root mean square errors and mean absolute error) show that the proposed methodology is suitable to estimate net radiation at surface from common meteorological variables, therefore, can be used as a substitute for net radiometers.