Modern military aircraft are fitted with Engine Monitoring Systems (EMS), which have the potential to provide maintenance personnel with valuable information for diagnosing engine faults and assessing engine condition. In this study, analytical redundancy methods have been applied to gas turbine engine transient data with the view to extracting the desired fault information. The basic idea is to use mathematical models to interrelate the measured variables and then monitor the effects of fault conditions on the new estimates of the model parameters. In most of the existing literature the models used are assumed to be perfect with the primary source of error arising from the measurement noise. In the technique to be described, a new method of quantifying the effects of changes in the operating conditions is presented when simplified models are employed. The technique accounts for undermodeling effects and errors arising from linearization of an inherently nonlinear system. Results obtained show a marked improvement over those obtained with traditional methods.