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This paper presents a neural recognition system for manufacturing applications in difficult industrial environments. In such difficult environments, where objects to be recognized can be dirty and illumination conditions cannot be sufficiently controlled, the required accuracy and rigidity of the system are critical features. The purpose of the real-time system is to recognize air-conditioning objects for avoiding deficiency in the manufactured process and erroneous identifications due to a large variety of size and kinds of objects. The architecture of the proposed system is based on several backpropagation neural networks in order to make an automatic recognition. Experimental results of a large variety of air-conditioning objects are provided to show the performance of the neural system in a difficult environment.