A growing body of research seeks to optimize the selection and scheduling of maintenance, repair and rehabilitation activities for networks of sections of pavement. Such research typically relies on a composite condition index, a one-dimensional and often discrete measure of the overall structural health and/or serviceability of pavement. Pavement can suffer from a large number of related but distinct distresses. Difficulties associated with unobserved heterogeneity have hampered efforts to accurately model deterioration via composite condition indices. At the same time, optimization techniques used in pavement management have been shown both to be sensitive to deterioration model specification and to become computationally intractable as condition data increase. This research describes how approximate dynamic programming can be used to manage a large network of related sections of pavement each one of which may be plagued by a number of different distresses. Approximate dynamic programming mitigates the curse of dimensionality that has haunted distinct Markov decision problem formulations of the infrastructure management problem and limited their complexity. A computational study illustrates how the proposed approach leads to more sophisticated maintenance decision rules, which can be used to ensure the suggestions of pavement management systems more closely match engineering best practices