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In this paper, a neural network (NN)-bacterial foraging algorithm (BFA) is proposed to control the load frequency of the power system. Traditionally, the tumbling decision element of BFA is defined by the stochastic values. But, these values are changed as per the variations of chemotactic step size so that the convergence time is increased. Here, NN is used to ensure the distribution of stochastic value with respect to the chemotactic step size and thus the performance of BFA is enhanced. The feed forward NN is used here with back propagation training algorithm. Using the proposed controller, the controller error, load changes, and speed changer position are tuned and the stability of the interconnected power system is improved. The proposed tuning controller is implemented in MATLAB/Simulink platform and the load frequency control responses are evaluated. The performances of proposed controller are compared with those of the PID controller and the BFA-PID controller. [ABSTRACT FROM AUTHOR]