In this paper we describe a word clustering method for class-based n-gram model. The measurement for clus-tering is the entropy on a corpus different from the cor-pus for n-gram model estimation. The search method is based on the greedy algorithm. We applied this method to a Japanese EDR corpus and English Penn Treebank corpus. The perplexities of word-based n-gram model on EDR corpus and Penn Treebank are 153.1 and 203.5 re-spectively. And Those of class-based n-gram model, esti-mated through our method, are 146.4 and 136.0 respec-tively. The result tells us that our clustering methods is better than the Brown’s method and the Ney’s method called leaving-one-out. 1.