We propose to add sets and maps to Standard ML. Our implementation uses hash-tries to code them, yields fast general-purpose set-theoretic operations, and is based on a run-time where all equal objects are shared. We present evidence that this systematic use of hash-consing, and the use of hash-tries to code sets, provide good performance. 1 Introduction Sets have been an adequate foundation for mathematics for nearly a century, and are also an important conceptual medium in computer science. Modern specification languages like VDM [18] and Z [30] are based on sets. But few programming languages provide general-purpose sets and maps: although they could be adequate for prototyping, it is feared that they would be too slow for real applications. We have designed and implemented an extension of Standard ML [17], called HimML 1 [12] providing fast general (polymorphic) set-theoretic data-structures, and a comprehensive set of efficient operations on them. After mentioning related work...