Multidimensional scaling (MDS) is a technique that represents proximities among objects as distances among points in a low-dimensional space. It allows researchers to explore similarity structures among objects (e.g., persons and variables) in a multivariate data set. Early MDS developments can be traced back to the late 1950s and the 1960s. In the 1970s, technical MDS details were worked out and important MDS extensions were proposed. During that time, MDS software was developed and a first peak of MDS applications was reached. Since then, MDS has been widely applied in fields like psychology, marketing, political sciences, ecology, and several others. In this entry, the basic principles of MDS are highlighted and extensions of MDS are examined.