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A Machine Translation (MT) system is an automatic process that translates from one human language to another language by using context information. We evaluate the use of an MT-based approach for query translation in an Arabic-English Cross-Language Information Retrieval (CLIR) system. We empirically evaluate the use of an MT-based approach for query translation in an Arabic-English CLIR system using the TREC-7 and TREC-9 topics and collections. The effect of query length on the performance of the machine translation is also investigated to explore how much context is actually required for successful MT processing.