This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations. To assess the efficiency and relevance of the proposed strategy, the controller is implemented on-line, experimentally validated on a real fuel cell and compared to the built-in controller. In this aim, several control scenarios are considered to evaluate the controller performance. Experimental results show the excellent tracking capability and disturbances rejection ability of the controller, regardless of the operating conditions. Moreover, compared to the built-in controller the proposed strategy demonstrates better disturbances rejection capability. Overall, the proposed neural model-based self-tuning PID controller appears as an excellent candidate to address the oxygen excess ratio regulation issue.