Efficient maintenance of road networks is an important factor for the smooth flow of traffic and is highly dependent on the timely assessment of road conditions. Road defects such as cracks and potholes cause significant safety and economic problems. Automation of these activities is vital for efficient and cost-effective maintenance of road networks. Image analysis techniques have widely been used and proven to be effective in fields such as medical image processing, face recognition, pattern recognition, and computer vision. We propose a two-stage modular approach for road defect recognition. The first module deals with image segmentation using a variant of random walk algorithm, which employs boost C-means clustering for selection of initial seeds. The second module conducts block-based image classification using an enhanced artificial neural network, which utilizes genetic algorithm for weight optimization. Experimental results show that the proposed approach is effective for the automatic recognition of pavement defects. The accuracy is not <73  %  , even when multiple defect categories are considered.