Nowadays, centrifugal compressors are commonly used in the oil and gas industry, particularly in the energy transmission facilities just like a gas pipeline stations. Therefore, these machines with different operational circumstances and thermodynamic characteristics are to be exploited according to the operational necessities. Generally, the most important operational parameters of a gas pipeline booster station includes the compressor's input and output pressures, input and output temperatures and also the flow rate passing from the compressors. Different values of those parameters related to every point of operational conditions will exactly affect on the compressor poly-tropic efficiency and their driver fuel consumption. Although, calculating of the poly tropic efficiency and fuel consumption using the existing thermodynamic relations, would need to apply rather awkward equations for each operating point. In this research, a feed forward perceptron artificial neural network is presented to predict the output operational conditions. The network would be trained at least in two scenarios applying by practical data in the neuro solution software version.5 using the Levenberg-Marquadt algorithm and the optimum model is experimentally selected according to R2, MSE and NMSE.