Functional programming has its merits, but imperative programming is here to stay. It is the most natural way to perform input and output. Some programs are specifically concerned with managing state: a chess program must keep track of where the pieces are! Some classical data structures, such as hash tables, work by updating arrays and pointers.

Standard ml's imperative features include references, arrays and commands for input and output. They support imperative programming in full generality, though with a flavour unique to ml. Looping is expressed by recursion or using a while construct. References behave differently from Pascal and C pointers; above all, they are secure.

Imperative features are compatible with functional programming. References and arrays can serve in functions and data structures that exhibit purely functional behaviour. We shall code sequences (lazy lists) using references to store each element. This avoids wasteful recomputation, which is a defect of the sequences of Section 5.12. We shall code functional arrays (where updating creates a new array) with the help of mutable arrays. This representation of functional arrays can be far more efficient than the binary tree approach of Section 4.15.

A typical ml program is largely functional. It retains many of the advantages of functional programming, including readability and even efficiency: garbage collection can be faster for immutable objects. Even for imperative programming, ml has advantages over conventional languages.