Recent hardware advances have rendered active vision a viable option for a diverse spectrum of applications ranging from MEMS manufacture toassisting individuals with disabilities. However, there are relatively few instances where these techniques have been successfully applied in uncontrolled environments. This can be traced to the fragility of active vision systems designed using classical methods. In this paper we show how a combination of robust identification and control synthesis techniques can be brought to bear on this problem. These results are experimentally validated using a robotic head.