We propose a memory-based processor called a Functional Memory Type Parallel Processor for vector quantization (FMPP-VQ). The FMPP-VQ is intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search on vector quantization. In the nearest neighbor search, we look for a vector nearest to an input one among a large number of code vectors. The FMPP-VQ has as many PEs (processing elements, also called "blocks") as code vectors. Thus distances between an input vector and code vectors are computed simultaneously in every PE. The minimum value of all the distances is searched in parallel, as in conventional CAMs. The computation time does not depend on the number of code vectors. In this paper, we explain the detail of the architecture of the FMPP-VQ, its performance and its layout density. We designed and fabricated an LSI including four PEs. The test results and performance estimation of the LSI are also reported.