Solid oxide fuel cells (SOFCs) have gained lots of attentions owing to their high energy conversion efficiency, however, because of the complex technology, their application is not mature as compared with other types of fuel cells such as proton-exchange membrane fuel cells and direct methanol fuel cells. The micro-structure is one of important factors on the SOFC performance, therefore, in order to expedite the commercialization of SOFCs, it is crucial to develop an effective method to optimize the complicated microstructure of SOFCs. The experiment of the SOFC performance test is time-consuming and cost-ineffective, thus it is necessary to develop an SOFC simulation model with high reliability to save the time and cost of the micro-structure optimization. This research proposes an artificial neural network (ANN)-based SOFC simulation model according to the experimental data of an anode-supported SOFC performance, in which the polarization characteristics of SOFCs are estimated from their structural characteristics. After training the ANN based on a part of the experimental data, the rest part of data are used to evaluate the effectiveness of the proposed SOFC model. Results show that the proposed SOFC simulation model accurately presents the polarization characteristics of SOFCs according to the structural characteristics, and this indicates that the model is suitable for the micro-structure optimization for SOFCs.