Translation Memory (TM) system, a major component of computer-assisted translation (CAT), is widely used to improve human translators' productivity by making effective use of previously translated resource. We propose a method to achieve high-speed retrieval from a large translation memory by means of similarity evaluation based on vector model, and present the experimental result. Through our experiment using Lucene, an open source information retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.