General motion planning of mechanical arm is only suitable for specific tasks, and can not interact with the environment immediately. To solve this problem, a mechanical arm is developed by the combination of multiaxis learning motion control and image technology, which meets the needs of various operational tasks. First, the VITE (Vector Integration to Endpoint) model was established to realize realtime trajectory planning. Then, the cerebellar model controller (CMAC) was used as a learning controller to cope with a more complex working environment. Subsequently, the target location was estimated by using two axis rotating focal length zoom camera. Finally, the image collection and arm control were performed by using National Instrument (NI) embedded digital signal processor and image acquisition card. Experiments show that the manipulator can achieve realtime tracking and grasping according to the object image. The motion trajectory error of the developed manipulator is smaller than ProportionDerivative (PD) controller, and its control is more precise and smooth.