A simplified, low order state-space approach for dynamic modelling of a twin spool turbofan gas turbine engine is proposed and investigated. The governing equations constituting the dynamic model of the engine are derived analytically by considering 1D mass, moment and energy balance equations at intermediate engine stations. Engine subsystems are modelled by using algebraic relationships, neural networks and experimental data collected through rig tests. Simulations are performed at various operation conditions for investigating the off-design and transient behaviour of the engine system. The simulation results are compared with the experimental time series data recorded during engine test runs. The responses of the model are found to be in good agreement with the experimental results for critical engine parameters such as shaft speeds, compressor delivery pressure, turbine inlet temperatures, etc. The proposed model may be extended for use in real-time engine simulation scenarios such as closed loop testing of engine control and condition monitoring systems. Copyright © 2010 Inderscience Enterprises Ltd.