This paper presents an efficient parallel algorithm for computing the mutual range-join of N sets of numbers on shared-nothing hypercube computers. The algorithm iteratively joins each set to the mutual range-join of the preceding sets. Each join is performed on all processors of the hypercube in parallel. The algorithm uses a global sorting method to distribute the elements of the first set evenly across all processors in increasing order, a new data balancing technique to distribute the elements of subsequent sets to match the intermediate set at each processor and to compensate for join skew, and a new efficient local range-join procedure. We analyse the performance of this algorithm and demonstrate that it improves on the best previously published algorithm for this problem when the join selectivity factor is small. The method can also be applied to similar problems such as band-join and equi-join.