Numerous methods have been proposed previously to describe the characterization of porous materials; however, no well-developed theory is still available. Three different modeling methods were employed in this study to explore the relationship between the characterization parameters of activated carbon (AC) and its methane uptake. The first and the second methods were based on the Radial Basis Function (R.B.F) neural networks. At the first R.B.F. modeling, the neural networks algorithm was designed using the Gaussian function. The collected data for modeling were divided into two parts; (i) the data used for training the network and (ii) the data used for testing the predicted network. At the second R.B.F. modeling, the MATLAB toolboxes for designing the R.B.F. neural networks were applied. The response surface method was employed as another model, using different functions in proportion to the way that the parameters affect the methane uptake. Concerning the error minimization in the estimation of the response and some statistical methods, the suitable model was selected. The results revealed that all these models were suitable for modeling the relation between the characterization parameters of the activated carbon and the methane uptake. However, the best response was provided by the neural networks modeling with the MATLAB toolboxes, demonstrating the smaller difference. (C) 2008 Elsevier Ltd. All rights reserved.