A neural network-based nonlinear intelligent control method was proposed for electro-optical (EO) tracking systems to overcome the performance reduction caused by the complex nonlinearity existent in real systems. A radial basis function neural network supervisory control structure was employed, and the associated advantages and characteristics were expatiated in the proposed study. Furthermore, a tracking experiment was conducted for performance evaluation. The obtained experimental results demonstrate that the disturbance attenuation performance can vary from -28 dB to -51 dB within the disturbance frequency of 1 Hz and amplitude of 3°, which indicates an improvement of 15 dB over the PID control method. The results also indicate that EO tracking technology based on neural network control possesses the advantage of intelligent optimized tracking by learning a system's nonlinear information without human intervention. Hence, compared to conventional tracking algorithms, neural network-based EO tracking technology can be incorporated more effectively in complex application environments.