The effort required to build a classifier for a task in a target language can be significantly reduced by utilizing the knowledge gained during an earlier effort of model building in a source language for a similar task. In this paper, we investigate whether unlabeled data in the target language can be labeled given the availability of labeled data for a similar domain in the source language. We view the problem of labeling unlabeled documents in the target language as that of clustering them such that the resulting partitioning has the best alignment with the classes provided in the source language. We develop a cross language guided clustering (CLGC) method to achieve this. We also propose a method to discover concept mapping between languages which is utilized by CLGC to transfer supervision across languages. Our experimental results show significant gains in the accuracy of labeling documents over the baseline methods.