Using a condition monitoring system to figure out whether or not a gas turbine is inclined to faulty condition provides useful help to determine the required preventive action before failure happening. System identification is a discipline in condition monitoring to learn the behavior of a healthy engine and employs it to predict the fault proneness. This study aims to discuss the performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) compared to the Artificial Neural Networks (ANNs) for the purpose of gas turbine performance identification. Toward this end, three system identification Bank of Networks (B-Ns), each network corresponding to one of the five commonly measurable variables on twin-shaft industrial gas turbine engines, are developed. The accuracy of the trained B-Ns are analyzed using the performance data of a healthy industrial 18.7 MW open-cycle offshore gas turbine. Making a comparison between the obtained results from ANFIS and two various ANNs models revealed that ANFIS model is able to forecast performance parameters with higher correlation coefficient and smaller MAPE values.