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The progressive reduction in permissible tailpipe emissions levels from automobiles has been achieved through the adoption of ever more complex engine control systems and aftertreatment components. This, in turn, has resulted in the development of increasingly sophisticated monitoring systems that can detect the failure or gradual degradation of any of these components and thereby fulfill the requirements of the stringent On-Board Diagnostic (OBD) legislation. Traditional monitoring techniques involve a physical model approach, which describes the system under investigation. This approach has limitations, such as available knowledge base and computational load. Neural networks, on the other hand, have been recognized as a powerful tool for modeling systems which exhibit nonlinear relationships between measured variables, such as internal combustion engines.