Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a lowdimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible. For this optimization procedure a new stress function with very good convergence properties is presented and efficiently implemented. The suitability of the proposed MDS for high-throughput data (HiT-MDS) is studied in applications to macroarray analysis for up to 12,000 genes. Key words: multi-dimensional scaling, clustering, gene expression analysis.