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This paper describes an experiment in the definition of tools for type inference and type verification of ML-like functional languages, using abstract interpretation techniques. We first show that by extending the Damas-Milner type inference algorithm, with a (bounded) fixpoint computation (as suggested by the abstract interpretation view, i.e. by a slight variation of one of the type abstract semantics in [7]), we succeed in getting a better precision and solving some problems of the ML type inference algorithm without resorting to more complex type systems (e.g. polymorphic recursion). We then show how to transform the analyzer into a tool for type verification, using an existing verification method based on abstract interpretation. The resulting type verification method can be exploited to improve the ML type inference algorithm, when the intended type of functions is specified by the programmer.