Thermodynamic diagnostics aim to identify and act upon thermal system devices exhibiting abnormal behaviour (malfunctions) in order to later, through the use of maintenance routines, return the devices to their optimum operating condition. Several methods have been developed to solve the problem of thermal system thermodynamic diagnosis, all of which are designed to identify components exhibiting malfunctions and their effect on cycle power output and efficiency. Individually these methods have both advantages and disadvantages, with the complementary use of two or more providing better results. In this paper a diagnostic system is proposed for externally fired gas turbines (EFGT), using the thermoeconomic method in conjunction with artificial neural networks to identify malfunctioning components (intrinsic malfunctions) and their fuel impact. The concepts "Exergetic Operator" and "Transition Structure" are also presented. An EFGT was simulated using the commercial software GateCycle((TM)) 5.51, aiming to reach a power of 99.80 kW (design point) using wood carbonisation residual gas as fuel. An ANN was developed with the commercial software MATLAB (R). (C) 2016 Elsevier Ltd. All rights reserved.