The performance of 0.5% wt Rh/-AL(2)O(3)catalyst for the dry reforming of natural gas using carbon dioxide has been considered. In the present work a comparative study has been performed using Radial Basis Function Neural Network (RBFNN) and Response Surface Methodology (RSM) quadratic models to investigate their predictive ability for the effect of two different operating parameters, naSSSmely the hourly space velocity and the reaction temperature on the conversion of the different components comprising commercial natural gas. The predictive capabilities of the two methodologies were compared employing statistical error functions. The results indicated the superiority of RBF in the prediction capability; for example, the F-ratio for the CH4 reactant is 86 and 1088185 employing RSM and ANN methods, respectively. Also for the various components involved in the reaction system R-2 ranges from 0.74 to 0.96 in case of RSM while it is 1.0 for all components employing ANN. This is due to ANN ability to approximate the non-linearity between the input and output variables.