The present study considered the amount of water-alcohol separation within the process of pervaporation by use of polymer membranes with the help of Artificial Neural Network modeling. Pervaporation can be utilized for separation of many liquids (in this study, it was used for separation of ethanol and butanol from water). All the mentioned alcohols have purity problems due to azeotropics with water. The experimental data were compared and analyzed with the model data. In this research, the effects of such parameters as Volumetric flow rate and temperature, as well as feedstuff properties (separation factor and flux) on the dehydration process efficiency were evaluated, and the Multi Layers Perceptron (MLP) neural network feed forward along with Propagation learning algorithm and Levenberg-Marquardt function with 2 inputs and outputs were implemented. Tansig activation algorithm was used for the hidden layer, and Purelin algorithm was utilized for the output layer. Furthermore, 5 neurons were defined for the hidden layer. After processing the data, 70 percent were allocated for learning, 15% were allocated for validity, and the remaining 15% were allocated for the experience. The achieved results with the aforementioned method had a suitable accuracy. The graphs of the error percentage for the actual values of the separation factor and flux outputs were compared to the achieved values from modeling through related membranes for evaluating the efficiency of pervaporation process in separation of ethanol and butanol from water. Finally, the graphs were drawn.