Besides the variety of fonts, character recognition systems for the industrial world are confronted with specific problems like: the variety of support (metal, wood, paper, ceramics . . .) as well as the variety of marking (printing, engraving, . . .) and conditions of lighting. We present a system that is able to solve a part of this problem. It implements a collaboration between two neural networks. The first network specialized in vision allows the system to extract the character from an image. Besides this capability, we have equipped our system with characteristics allowing it to obtain an invariant model from the presented character. Thus, whatever the position, the size and the orientation of the character during the capture are, the model presented to the input of the second network will be identical. The second network, thanks to a learning phase, permits us to obtain a character recognition system independent of the type of fonts used. Furthermore, its capabilities of generalization permit us to recognize degraded and/or distorted characters. A feedback loop between the two networks permits the first one to modify the quality of vision.The cooperation between these two networks allows us to recognize characters whatever the support and the marking.