This paper presents a dynamic model for Solid Oxide Fuel Cell (SOFC) stack using Local Linear Model Tree (LOLIMOT) algorithm which is useful for both dynamic and steady-state studies. Most of conventional SOFC models require a large number of parameters and factors, which are difficult to be determined or estimated. In this paper, a LOLIMOT-based model, which does not require the parameters of fuel cell, is proposed for each operation mode of SOFC. In these models, decision tree-based feature selection approach is exploited to select inputs of the LOLIMOT. The proposed models are trained in a short time and they have little errors. In order to illustrate the effectiveness of the proposed models, they are applied to a simulated model of a 5-kW SOFC stack which show satisfactory results for both steady-state and dynamic studies. Furthermore, the proposed model demonstrates convincing results for real-time simulation studies. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.