Pavement maintenance requires knowing the state of the road surface. Human inspection is the most common method for evaluating this state. Recently, the automated visual inspection has been addressed, but some important questions remain open concerning the variable ambient lighting, shadows, device synchronisation and the large amount of data. In the present paper, an automated visual inspection system is presented. Images are obtained using laser lighting and linear cameras onboard a vehicle. Longitudinal and transversal cracks are detected and classified using a novel approach based on combining traditional features and Gabor filters. A Differential Global Positioning System (DGPS), a web camera and an Inertial Profiler to measure the International Roughness Index (IRI) are also considered in order to obtain comprehensive information about the road state. Implementation details are given concerning image acquisition and processing, system architecture and data synchronisation. Field results are presented which prove the suitability of the approach."