In this work, an artificial neural network (ANN) has been utilized to predict the surface tension of binary mixtures at different temperatures and concentrations and at atmospheric pressure. It has been shown, that a multilayer perceptron network (MLP) can be trained better than other types of ANNs, and it can therefore be used as a predictive tool to predict the thermo-physical properties. In the modeling procedure, 60% of the available experimental data has been selected as the training set; the remaining data has been used to test and validate the network. After training and testing, the artificial neural network has been used for the prediction of the surface tension of a number of other systems, for which a minimum imprecision of 1.8% has been obtained. The results obtained from the trained network have also been compared to those obtained from the Sprow and Prausnitz model [Sprow, F.B., Prausnitz, J.M., Surface tensions of simple liquid mixtures. Trans. Faraday Soc. [966, 62a 1097-1104]. It has been shown that the trained MLP network can predict the experimental data better than the r conventional neural network method while only a minimum number of adjustable parameters have been used, compared to the number of adjustable parameters in the thermodynamics models, such as the Sprow and Prausnitz model.