In this work, artificial neural network (ANN) was utilized to develop a new model for the predictionof the kinematic viscosity of petroleum fractions. This model was generated as a function oftemperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to developthe new model, different architectures of feed-forward type were examined. Finally, the optimumstructure with three hidden layers was selected. The optimum structure had five, four, and twoneurons in the first, second, and third layers respectively. To prevent over-fitting problem, 70% of theexperimental data were used to train and validate the new model and the remaining data which did notparticipate in learning process was utilized to test the ability of the new model for the prediction of thekinematic viscosity of petroleum fractions. The results showed that the predicted/calculated andexperimental data are in good agreement. The average absolute relative deviation (AARD) of the newmodel was 1.3%. Finally, the results were compared with an Eyring-based model (Soltani et al.’swork); it was shown that, based on the reported results by the authors, the accuracy of both modelwere in the same order.