To prevent the destructive surge phenomenon in centrifugal compressors, we usually choose the surge control line in a prudent manner, which in turn cause in reducing the efficiency of compressor and results in increasing energy consumption of its actuator. It requires a roughly accurate model of system performance to design an optimum controller. In this paper we introduce a model based on artificial neural networks. It gets the compressor RPM, anti-surge valve state, temperatures, compressor gas output flow rate and its upstream pressure as inputs. We have used the measurements conducted by cybernetics laboratory of Norway University from a centrifugal compressor installed in a gas processing plant as our dataset. We have achieved better results in predicting the system output in comparison to mathematical model based on state equations.