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PID controllers are still popular in a wide range of engineering practices due to their simplicity and robustness. Traditional design of a PID controller needs manual setting of its parameters in advance. This paper proposes a new method for online tuning of PID controllers based on hybridized neural membrane computing. A neural network is employed to adaptively determine the proper values of the PID parameters in terms of evolving situations/stages in the control process. Further the learning of the neural network is performed based on a membrane algorithm, which is used to locate the weights of the network to optimize the control performance. The effectiveness of the proposed method has been demonstrated by the preliminary results from simulation tests.