Overrides of credit ratings are important correctives of ratings that are determined by statistical rating models. Financial institutions and banking regulators agree on this because, on the one hand, errors with ratings of corporates or banks can have fatal consequences for the lending institutions and, on the other hand, errors by statistical methods can be minimized but not completely avoided. Nonetheless, rating overrides can be misused in order to conceal the real riskiness of borrowers or even entire portfolios. This is why rating overrides are usually strictly governed and carefully recorded. It is not clear, however, what frequency of overrides is appropriate for a given rating model within a predefined time period. This paper argues that there is a natural error rate associated with a statistical rating model that may be used to inform the assessment of whether or not an observed override rate is adequate. The natural error rate is closely related to the rating model's discriminatory power and can readily be calculated.