The definition of Standard ML provides a form of generic equality which is inferred for certain types, called equality types, on which it is possible to define a computable equality relation. However~ the standard definition is incomplete in the sense that there are interesting and useful types which are not inferred to be equality types but which nevertheless have a computable equality relation. In this paper, a refinement of the Standard ML system of equality types is introduced and is proven sound and complete with respect to the existence of a computable equality. The technique used here is based on an abstract interpretation of ML operators as monotone functions over a three point lattice. It is shown how the equality relation can be defined (as an ML program) from the definition of a type with our equality property. Finally, a sound, efficient algorithm for inferring the equality property which corrects the limitations of the standard definition in all cases of practical interest is demonstrated.