Abstract: Currently, a machine vision system cannot take the full image of the workpiece with the size larger than the visual field of a single camera in the process of workpiece inspection. Thus, it has caused some problems such as workpiece recognition difficulty and inaccurate positioning. To solve these problems, this paper proposes an exceeding-field workpiece recognition and grabbing system (WRGS) based on machine vision. The system can realize the effective recognition of the exceeding-field workpiece by extracting the feature points on workpiece and conducting shape matching, without shooting the full image of the workpiece. The system defines the standard location of each type of workpiece for position calculation, and designs the coarse-to-fine grabbing network method to solve the positioning difficulty of exceeding-field workpieces and improve the precision of grabbing. Besides, the system can adapt to a variety of workpieces through self learning, and thus enhances the flexibility. Experiment results show that the system grabbing time is less than 6s; the errors between the claw's position and the target in the horizontal direction, in height and angle are 2mm or less, 0.2mm or less, and 0.3° or less respectively.