In this study, thermal performance of solar air collector system which was experimentally constructed was obtained for different operating conditions. Experiments were conducted under Turkey/Mersin climatic conditions. Then, Neural Network (NN) models have been developed for the prediction the thermal performance of solar air collectors. Experimental data were used for training and testing of the networks. The inputs of the network are inlet and outlet air temperature to collector, solar radiation and air mass flow rate and the output is thermal performance of solar air collector. Using the weights obtained from the trained network a new formulation is presented for the calculation of the performance; the use of NN is proliferating with high speed in simulation. The R2-values obtained when unknown data were used to the networks was 0.9985 which is very satisfactory. The use of this new formulation, which can be employed with any programming language or spreadsheet program for the estimation of the thermal performance of solar air collectors, as described in this paper, may make the use of dedicated NN software unnecessary.