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EÆcient storage and query processing of data spanning multiple natural languages are of crucial importance in today's globalized world. A primary prerequisite to achieve this goal is that the principal data repositories, relational database systems, should eÆciently and seamlessly support multilingual data. Our survey of current relational systems indicates that while they do support storage and management of multilingual data, querying is restricted to be within a given language, with no crosslingual query support. Further, quantitative performance study of the systems working on di erent character sets has not been published so far and therefore is an open issue. In this thesis, we rst pro le the multilingual performance of a set of current relational database systems, using an environment based on the TPC benchmark suites. The results indicate a signi cant performance degradation while handling multilingual data. While the di erential performance is huge when disk traÆc is a factor, it is substantial even when only in-memory processing is considered. To address this inequity, we propose a split representation format that reduces the multilingual storage space and largely eliminates the di erential performance for most languages except those with unusually large repertoires. Next, we propose functionality enhancements that complement the standard lexicographic matching, speci cally in the multilingual text space. Two new multilingual join operators { one for joining names across languages and the second for joining multilingual categories based on their meanings { are proposed and formally de ned. These operators are implemented in an outside-the-server approach using existing SQL features of relational systems, and using standard linguistic resources. While the performance