Model-based diagnostic techniques can be used success- fully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as noise in the signals and errors in the evaluation of the experimental vibrations that are caused only by the impending fault, can affect the accuracy of the fault iden- tifications. This can make it difficult to identify the type of actual fault as well as to evaluate with care its severity and position. This article shows some techniques that have been developed by the authors to measure the accuracy of the results obtained with model-based identification meth- ods aimed to diagnose faults in rotating machines. In this article, the results obtained by means of the analysis of ex- perimental data collected in a power plant are described. Finally, the capabilities of the developed methods are shown and discussed. Usually, the model of the fully assembled machine allows the response of the rotors, bearings, and the foundation structure to be simulated. The rotor train can be modeled with beam finite elements (FE), while the dynamic effects of the faults can be simulated with suitable sets of equivalent forces and moments that are applied to the nodes of the FE model of the rotors. Therefore, the identification of a fault can be obtained by evalu- ating the system of excitations that minimizes the error between the machine experimental response and the numerical response evaluated with the model. Usually, this error is called residue. In order to make the comparison among the results of different analyses easier, a relative residue is evaluated. Weighted least squares methods can be used to identify the equivalent forces and moments that simulate the faults. Although the vibrations of the shafts along the spans between two adja- cent supports can give very useful information to identify the machine faults, usually, in real machines, only shaft and support vibrations measured on the journal bearings are available. This requires more careful fault identification analyses to be carried out. Unfortunately, also in the absence of faults, the rotating ma- chines are subjected to vibrations that, generally, are caused by a residual unbalance of the rotors as well as by misalignments of couplings and supports. These excitations cannot be easily modeled as they are unknown. Therefore, fault identification techniques require one to evaluate the transient vibrations of the machine obtained by subtracting the vibrations measured in normal state from those measured after the fault occurrence. If the non linearity of the system is negligible, these transient ad- ditional vibrations represent the machine response caused only by the fault. However, if the two speed transients occur in dif- ferent thermal conditions of the machine or if other factors, in addition to the malfunction, affect the system vibrations, the accuracy of the results of the fault identification can be poor. Similar effects can be caused by the presence of noise in the vi- bration signals. Moreover, the accuracy of the results of model- based techniques can be significantly influenced by the care with which the model of the fully assembled machine has been de- veloped. Usually, a preliminary careful tuning of the model is