In the last decades, a growing number of image analysis applications in the process and food industries have been reported. This is because artificial vision systems can return a quick, accurate, and objective indication. of the quality of the. manufactured end product. However, reproducibility of the image analysis results is ensured only as long as the conditions, under : which the images used for the calibration of the quality assessment model were collected, do not change during normal operation of the manufacturing process. These conditions include the status of the artificial vision illuminating system and of the camera sensor. In this paper, we specifically deal with the issues related to the aging or failure of the illuminating system. First, a strategy is developed to monitor the status of the machine vision system, in order to detect any changes in the lighting conditions. Then,. two alternative strategies are proposed to avoid recalibrating the quality assessment model in case a new lighting condition is, detected. The first strategy aims at determining a new combination of camera settings to compensate for the change in the light illuminating the subject, whereas. the second one adapts the :available model to the novel conditions: The effectiveness of the. proposed strategies is tested on a pharmaceutical manufacturing case study involving quality assessment on film coated tablets.