This study deals with modelling the performance of an R134a automobile air conditioning (AAC) system by means of adaptive neuro-fuzzy inference system (ANFIS) approach. In order to gather data for developing the ANFIS model, an experimental AAC system employing a variable capacity swash plate compressor and a thermostatic expansion valve was set up and equipped with various instruments for mechanical measurements. The system was operated at steady state conditions while varying the compressor speed, dry bulb temperatures and relative humidity of the air streams entering the evaporator and condenser as well as the mean velocities of these air streams. Then, utilizing some of the experimental data, an ANFIS model for the system was developed. The model was used for predicting various performance parameters of the system including the air dry bulb temperature at the evaporator outlet, cooling capacity, coefficient of performance and the rate of total exergy destruction in the refrigeration circuit of the system. It was determined that the predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.966-0.988 and mean relative errors in the range of 0.23-5.28%. The results reveal that the ANFIS approach can be used successfully for predicting the performance of AAC systems.