An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combines a fully parallel vector quantizer with a pipelined codebook generator for a broad area of data compression applications. The NNVQ module is capable of producing good-quality reconstructed data at high compression ratios more than 20. The vector quantizer chip has been designed, fabricated, and tested. It contains 64 inner-product neural units and a high-speed extendable winner-take-all block. This mixed-signal chip occupies a compact silicon area of 4.6 × 6.8 mm2 in a 2.0-μm scalable CMOS technology. The throughput rate of the 2-μm NNVQ module is 2 million vectors per second and its equivalent computation power is 3.33 billion connections per second.