We report the utilization of ZnO nanowires (NWs)-based e-nose towards successful discrimination of binary gaseous mixture comprising H₂S and NO₂ gases. In particular, analysis of individual components in the binary mixture of gases has been carried out using different pattern recognition algorithms (PRA) or models. Of these, principal component analysis (PCA) indicated a successful discrimination of the gases. The maximum variance of three principal components were found to be 95.89, 3.53, and 0.56%, respectively. To cross validate the results, hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) studies have also been performed. Herein, by estimating the probability of the classes, an accurate prediction of the gases with minimal misclassification was achieved. Thus, using sequential application of the three basic PRAs on the data repository, a successful discrimination of the individual component of the binary mixture of gases was accomplished.