A framework for fault diagnosis, called structured hypothesis tests, is presented. It has earlier been developed within the area of automatic control, but is in fact very much inspired by the ideas developed in the AI area. The motivation was originally to handle dynamic systems with noise. However, it is here shown that also the noise-free case can be perfectly handled. The system to be diagnosed, and also the different faults, are described by differential equations, algebraic equations, and probability distribution functions. By using the framework, it is in the isolation possible to utilize all such modeled knowledge about the faults. The diagnosis system is constructed by combining a set of different hypothesis tests. In this way, the task of diagnosis is transferred to the task of validating a set of different models with respect to the measured data.