In this paper we propose an Adaptive Network based Fuzzy Inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced as compared to the number of data required for the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.