In such a space where human workers and industrial robots work together, it has become necessary to monitor a robot motion for the safety. For such robot surveillance, we propose a robot tracking system from multiple view images. In this system, we treat tracking robot movement problem as an estimation problem of each pose parameter through all frames. This tracking algorithm consists of four stages, image generating stage, estimation stage, parameter searching stage, and prediction stage. At the first stage, robot area of real image is extracted by background subtraction. Here, Yuv color system is used because of reducing the change of lighting condition. By calibrating extrinsic and intrinsic parameters of all cameras with Tsai's method, we can project 3D model of the robot onto each camera. In the next stage, correlation of the input image and projected model image is calculated, which is defined by the area of robots in real and 3D images. At third stage, the pose parameters of the robot are estimated by maximizing the correlation. For computational efficiency, a high dimensional pose parameter space is divided into many low dimensional sub-spaces in accordance with the predicted pose parameters in the previous flame. We apply the proposed system for pose estimation of 5-axis robot manipulator. The estimated pose parameters are successfully matched with the actual pose of the robots.