This paper presents a method for combining sequential neural-network approximation and orthogonal arrays (SNAOA) in determining the major operation and design parameters which affect the performance of proton exchange membrane fuel cells (PEMFC). An orthogonal array was first conducted to obtain the initial solution set. The results obtained from the orthogonal array were then used as the experimental data for response surface methodology (RSM) that could predict the operation parameters at discrete levels. The set was then treated as the initial training sample and a backpropagation sequential neural network was trained to simulate the feasible domain for seeking optimal operation parameters of PEMFC. With this method, the size of the training sample was greatly reduced due to the use of the orthogonal array. In addition, a restart strategy was also incorporated into the SNAOA so that the searching process could have a better opportunity to reach a near global optimum with the objective of reaching maximum output power of the PEMFC, which has a separate flow field in the cathode. The major parameters harnessed in this study include operating temperature, humidification temperature, reactant flow rate, split point, and split flow rate. According to this novel methodology, the optimal parameters with a maximum power output were: operating temperature 78C, anode humidification temperature 72C, anode flow rate 296 sccm, cathode flow rate 295 sccm, split flow rate 145 sccm and split point 44%.