The vision-based hand tracking and gesture recognition is an extremely challenging problem due to the intricate nature of hand gestures this is a reason that available computer vision algorithms are computationally complex. In this research work a new methodology for 3D human hand gestures detection and recognition is proposed, which can be used for natural and intuitive human-computer interaction and other robotic systems. The proposed method based on morphology approaches to solve the problem of human hand tracking and gesture recognition of 3D objects from a single silhouette image. This new proposed method was applied and tested on the simulated Manipulated Robotic System (UniMAP Robot Manipulator Simulation System) that allows this robotic system to act as an intelligent system to track a human hand in 3D space and estimate its orientation and position in real time with the goal of ultimately using the algorithm with a robotic spherical wrist system. During experiment, there was no need for continuous camera calibration, experimental result shows that proposed method is a robust, unlike other approaches that use costly leaning functions or generalization methods.