This paper describes an investigation into the performance of three Ada packages for automatic differentiation. Two of these implement the forward accumulation approach while the third employs reverse accumulation. Each package is used to provide gradient information required by a number of optimization calculations, including examples of unconstrained, constrained and least-squares problems. The results show how automatic differentiation methods can be influenced in practice by the size, complexity and sparsity of a problem. They also demonstrate ways in which the methods should interface with different types of optimization procedure. Finally, and perhaps most significantly, the results show how the performance of automatic differentiation codes can depend on hardware and system software considerations that are sometimes ignored by numerical mathematicians.