A solution to the problem of homograph (words with multiple distinct meanings) identification is proposed and evaluated in this paper. It is demonstrated that a mixture model based framework is better suited for this task than the standard classification algorithms – relative improvement of 7% in F1 measure and 14% in Cohen’s kappa score is observed.