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A novel self-organizing neural network called the evolving tree is applied to classification of defect images. The evolving tree resembles the self-organizing map (SOM) but it has several advantages over the SOM. Experiments present a comparison between a normal SOM, a supervised SOM, and the evolving tree algorithm for classification of defect images that are taken from a real web inspection system. The MPEG-7 standard feature descriptors are applied. The results show that the evolving tree provides better classification accuracies and reduced computational costs over the normal SOMs.