In this study, an artificial neural network (ANN) tool, which uses the data obtained from a pore network (PN) model, was developed in order to obtain three-phase relative permeability values. During the development of this ANN tool, four different stages were implemented in which ANN structures were changed in order to find the best architecture that would predict the oil isoperms correctly. By using the data obtained from the PN model, training was implemented and the prediction power of that tool was tested. When the data obtained from PN and ANN tools were compared, it has been found that irrelevant variables affected the ANN model negatively as decreasing its ability to learn perfectly. Finally, it has been observed that trends of the isoperms were effectively predicted and the overall quality of predictions was improved by changing the ANN structure.