In this paper, we describe the application of a combined neocognitron
type of the neural network classifier in a generic Car License Plate Recognition (CLPR) system. The suggested system contains an image processor, a segment processor and five conpled neocognitron network classifiers that act as a character recognizer. The presented model of the system depends neither on the specific license plate image features nor on the license plates character style and size. Combining neocognitron classifiers were motivated by the fact that manually tuning a training set for a large neocognitron network is tedious. It is shown how the training set tuning for a large neocognitron network can be avoided. By connecting srnall neocognitrons specifically trained on ambiguous character classes, the performance of the recognizer in our CLPR was improved easily. The use of a neocognitron recognizer contributes significantly to the generality of a CLPR systém. Besides, character recognition rates of 94% are realized using the proposed neocognitron.