The paper describes the activities carried out for developing and testing Back Propagation Neural Networks (BPNN) for the gas turbine engine diagnostics. One of the aims of this study was to analyze the problems encountered during training using large number of patterns. Each pattern contains information about the engine thermodynamic behaviour when there is a fault in progress. Moreover the research studied different architectures of BPNN for testing their capability to recognize patterns even when information is noised. The results showed that it is possible to set-up and optimize suitable and robust Neural Networks useful for gas turbine diagnostics. The methods of Gas Path Analysis furnish the necessary data and information about engine behaviour. The best architecture, among the ones studied, is formed by 13, 26 and 47 neurons in the input, hidden and output layer respectively. The investigated Nets have shown that the best encoding of faults is the one using a unitary diagonal matrix. Moreover the calculation have identified suitable laws of learning rate factor (LRF) for improving the learning rate. Finally the authors used two different computers. The first one has a classical architecture (sequential, vectorial and parallel). The second one is the Neural Computer, SYNAPSE-1, developed by Siemens.