This paper describes a hash partitioning strategy for distributed query processing in a multi-database environment in which relations are unfragmented and replicated. Methods and efficient algorithms are provided to determine the sets of relations that can be hash partitioned, the copies of the relations to be partitioned and the partition sites, how the relations are to be partitioned and where the fragments are to be sent for processing. For a given query, there are usually more than one set of relations that can be hash partitioned. Among the alternatives, our algorithm picks the plan that gives the minimum response time. The paper also presents a simulation study that compares the hash partition strategy to the PRS strategy. The study shows that our strategy outperforms the PRS strategy.