This paper discusses the development and the implementation of a neural network for the condition rating of rigid concrete pavements. The condition rating scheme employed by Oregon State Department of Transportation was used as the basis for the development of the network presented. A training set of 298 cases was used to train the network. The network adequately learned the training examples with an average training error of 0.011. A testing set of 3902 cases was used to check the generalization ability of the system. The network was able to determine the correct condition ratings with an average testing error of 0.022. The network ability of dealing with noisy data was also tested. Up to 40% noise was added to the data and introduced to the network. The results showed that the network presented could accurately identify condition rating relationships at high level of noise. Finally, a statistical hypothesis test was conducted to demonstrate the system's fault-tolerance and generalization properties. Key words: neural networks, condition rating, condition index, rigid pavements, pavement distresses, pavement maintenance, fault-tolerance, generalization, noisy data.