Network incidents are largely due to configuration errors, particularly within network service providers who manage large complex networks. Such providers offer virtual private networks to their customers to interconnect their remote sites and provide Internet access. The growing demand for virtual private networks leads service providers to search for novel scalable approaches to locate incidents arising from configuration faults. In this paper, we propose a machine learning approach that aims to locate customer connectivity issues coming from configurations errors, in a BGP/MPLS IP virtual private network architecture. We feed the learning model with valid and faulty configuration data and train it using three algorithms: decision tree, random forest and multilayer perceptron. Since failures can occur on several routers, we consider the learning problem as a supervised multi-label classification problem, where each customer router is represented by a unique label. We carry out our experiments on three network sizes containing different types of configuration errors. Results show that multi-layer perceptron has a better accuracy in detecting faults than the other algorithms, making it a potential candidate to validate offline network configurations before online deployment.