The main purpose of catalytic reforming unit is to upgrade low-octane naphtha to high-octane gasoline. In this work, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), to determine the research octane number (RON) of reformates produced from the catalytic naphtha reforming unit were investigated. The article presents a comparative study and combined application between response surface methodology (RSM) and artificial neural networks (ANN) based on design of experiment (DOE) strategy in the modeling and prediction of the research octane number (RON). In this study, DOE-CCRD full factorial design was incorporated into the ANN methodology, so the need of a large quantity of training data was avoided. ANN methodology showed a very obvious advantage over RSM for both data fitting and estimation capabilities. Based on the results of analysis of variance (ANOVA), a multiple determination coefficient of 0.8 and 0.99 were obtained for both RSM and ANN respectively. It has been found that by employing RSM approach coupled with ANN model based on DOE strategy, the visualization of the experimental points in three dimensional spaces can disclose qualitatively and quantitatively the activity relationships. This approach of combination of RSM-ANN-DOE has revealed its ability to solve a quadratic polynomial model involving solving, optimization, complexity and difficult relationships especially nonlinear ones may be investigated without complicated equations involved. The study revealed that, the maximum RON of 88 was obtained at the optimum conditions offered by RSM. Furthermore, at the optimal conditions of (T=521 degrees C, P = 37.6 bar, LHSV= 2.02 h(-1)), the maximum RON of 98 was obtained for the ANN model. However, the models were implemented for the construction of 3D response surface plots from the ANN and RSM models in order to show the most effective variables as well as the effects of their interaction on the research octane number. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.