Real time monitoring of laser welding has a more and more importance in several manufacturing processes ranging from automobile production to precision mechanics. Despite the huge improvement in welding technology, sophisticated image based closed loop control systems have not been integrated in commercially available equipments yet. Due to the high dynamics of laser beam welding (LBW) processes, robust closed loop control systems require fast real time image processing with frame rates in the multi kilo Hertz range. In the last few years, some new high speed Cellular Neural Network (CNN) based algorithms for the full penetration hole detection in keyhole welding processes have been introduced. In particular, they can be distinguished in two categories: Orientation dependent and orientation independent algorithms. The former can be used only for the welding of straight lines, while the latter has been implemented for the control of curved weld seams. Both algorithms have been used to build up a real time closed loop control system for LBW processes. An algorithm comparison by the description of some experimental results is addressed in this paper.