Most current language recognition systems model different levels of information such as acoustic, prosodic, phonotactic, etc. independently and combine the model likelihoods in order to make a decision. However, these are single level systems that treat all languages identically and hence incapable of exploiting any similarities that may exist within groups of languages. In this paper, a hierarchical language identification (HLID) framework is proposed that involves a series of classification decisions at multiple levels involving language clusters of decreasing sizes with individual languages identified only at the final level. The performance of proposed hierarchical framework is compared with a state-of-the-art LID system on the NIST 2007 database and the results indicate that the proposed approach outperforms state-of-the-art systems.