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A novel VLSI efficient image compression technique employing fuzzy-ART neural network and 2D run-length encoding is presented. This technique involves the segmentation of the original image into smaller regular blocks and these blocks are subsequently applied to the fuzzy-ART network for classification. The class indices generated by the fuzzy-ART network are further reduced with 2D run-length encoding. For the implementation of the fuzzy-ART network in VLSI, a force class fuzzy-ART network had been derived, where the maximum number of possible output classes is fixed. In this new network, input vectors will be forced into its closest class, when all classes are occupied. The results for force class fuzzy-Art network demonstrate that it is capable of large compression ratios and this network can easily be ported into hardware architecture.