Microassembly has become an important technique to fabricate micro devices with different materials, complex shapes, and incompatible processes. Vision-based microassembly is a promising technique for automated microassembly. However, conventional vision-based techniques in microassembly are limited by inherent problems such as a small depth-of-field (DOF) and a narrow field-of-view (FOV). Microassembly operations initially need to detect micro parts in a wide FOV and a large DOF yet also maintain high resolution for the final state. A tradeoff between the DOF (and/or FOV) and resolution prevents the conventional systems from satisfying the aforementioned microassembly requirements. This paper presents an active zooming control method that enables dynamic adjustment of the DOF (FOV) according to the position and the focus measure of micro objects. The proposed method is based on an artificial potential field method with the capability to combine different kinds of constraints such as the FOV, the focus measure, and the joint limits into the system. The stability and robustness of the proposed system are also investigated. The novelty of this method is that it can ensure the vision system maintains a wide FOV and a large DOF initially, and high resolution at the end. Simulations and microassembly experimental results are provided to verify the feasibility of the proposed approach. Note to Practitioners-Vision-based techniques are now an integral part of microassembly systems. During microassembly, a vision system initially needs to detect micro parts which are usually spread over a large area, yet maintain high resolution for the final state. The tradeoff between DOF (FOV) and resolution, common to many vision-based microassembly systems, does not allow the fulfillment of this requirement. In this paper, an active zooming control method is proposed to adjust the FOV and DOF dynamically according to the position and focus measure of micro objects. Several constraints such as FOV, focus measure, and joint limits are integrated by the proposed method using an artificial potential field. Stability and robustness issues, central to realistic automation applications, are also analyzed. The simulation and experimental results verify potential application of the proposed method in vision-based microassembly.