Cellular neural networks (CNN) are more and more attractive for closed-loop control systems based on image processing because they allow for the combination of high computational power and short feedback times. This combination enables new applications, which are not feasible for conventional image processing systems. Laser beam welding (LBW), which has been largely adopted in the industrial scenario, is an example for such processes. Concerning the latter, monitoring systems using conventional cameras are quite common, but they do a statistical postprocess evaluation of certain image features for quality control purposes. Earlier attempts to build closed-loop control systems failed due to the lack of computational power. In order to increase controlling rates and decrease false detections by a more robust evaluation of the image feature, strategies based on CNN operations have been implemented in a cellular architecture called Q-Eye. They allow enabling the first robust closed-loop control system adapting the laser power by observing the full penetration hole (FPH) in the melt. In this paper, the algorithms adopted for the FPH detection in process images are described and compared. Furthermore, experimental results obtained in real-time applications are also discussed.