In many control contexts, such as vision-based control, data-processing methods are needed to distill information from measurement data (such as images). These data-processing methods introduce several undesirable effects such as delays, measurement inaccuracies and possible absence of information, which limit closed-loop performance. Typically, a single processing method with an appropriate compromise between these effects is chosen in practice. Instead of settling for a compromise using only one fixed processing method, we propose to break the design trade-off by switching on-line between several data-processing methods having different delay, accuracy, and data-loss characteristics. We provide a modeling framework for sensing and data-processing methods that is suitable for control applications and incorporates the characteristics of the undesirable effects mentioned above. Using the models provided by the framework, we provide explicit policies for switching on-line between sensing methods with different characteristics based on a modified rollout strategy. Our approach formally guarantees that an LQG-type infinite horizon performance is better than, or at least not worse than, non-switching approaches. The advantages of the proposed methodology are further highlighted via a numerical example. (C) 2019 Elsevier Ltd. All rights reserved.