This paper discusses the robustness of the artificial immune recognition system (AIRS) for the gases/odors identification problem. The steady state responses of a thick-film sensor array with four sensor elements with exposure of four gases, viz., H2, CO, CH4, and LPG, are used as input data. The AIRS algorithm with its versions including AIRS1, AIRS2, and parallel AIRS is applied to classify the unseen gases/odors data with duly trained networks. The classification accuracy of the AIRS algorithm is compared with radial basis function neural network, naive bayes, and learning vector quantization methods. The results obtained with the AIRS are found more promising in this experiment. The results are verified using a cross-validation scheme.