In this Paper, object tracking system model was developed using Neuro-Fuzzy hybrid based approach to predict the trajectory of an object moving around a scene. Servo motors were used to perform high-precision positioning in azimuth and elevation directions, fuzzy logic is applied to control the position servo motors via feedback. A Neuro-Fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. In particular, ANFIS methodology was used to build a Sugeno fuzzy model for controlling the servo motor position carrying charge couple device camera (CCD) on a chaotic trajectory. An advanced test bed is used in order to evaluate the tracking properties and the robustness of the ANFIS controller operations. However, the variations of the Mechanical configuration of the drive, which is common to these two applications, can lead to error in object positioning before segmentation. The result for the azimuth and elevation time responses show that the rise time tr reduces to 0.1 and 0.3, respectively. The settling time decreases to 0.5 for the motors with ANFIS controller, the delay time reduces to 0.1 for both motors. Steady state was reached. Conclusively, ANFIS controller output was the best in terms of faster rise time, settling time, reduced delay time and object position stabilization.