In this study, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults of a gas turbine system is presented. The diagnosis scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. The work is completed under both noise-free and noisy conditions. Residual analysis and statistical tests are used for fault detection and isolation, respectively. The final section shows how the actual size of each fault can be estimated using a multilayer perceptron neural network used as a nonlinear function approximator. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model.