This paper describes a method for word sense disambiguation using a similarity metric. In this method, we first obtain context-similarity vectors for the senses of a polysemous word using a corpus and also define the context representation for the same polysemous word appearing in text. We then calculate distributional matrix between each context-similarity vector and the context representation for the word to be disambiguated. Finally, comparing the values of distributional matrices, we select the sense with the highest value as the meaning of the polysemous word. An experiment with 682 instances for 10 polysemous words shows that we are able to disambiguate at a rate of almost 92%.