This paper will describe the distributed industrial inline application “broken roll detection”, which is placed in a really harsh industrial environment, with all aspects from the sensing base to algorithm, implementation and technology. In a seamless steel tube production the pipe shells produced in the punch bench are running through many roller stands (3-roll system) to get the final dimension. If one of the rolls is broken, structural voids near the surface are the consequence. So finding the structure voids on the tube means to find broken rolls. Since pipe shells are hot (approximately 900°C) after passing the rolls, temperature distribution on its surface is different when voids happen. This gives a good base for detecting such voids by watching the surface temperature by sensing the radiation at wavelengths from 0,7 to 1.1μm, which means that standard line scan cameras (3 x 2048 pixels, 10kHz line rate) can be used. Images of up to 600MB are the result for each imaged pipe shell. Evaluation of image data is done stepwise (in a pipeline) and on a separate channel for each camera with the objective to reduce data at each step. Images are detruncated, position-normalized, filtered, segmented and converted into object-descriptions that are sent to another PC for evaluating periodic occurrences. Once found such a periodic occurrence, the system signalizes it to the production line to stop the machine and repair the broken roll.