Tracing the chemical composition of the surrounding environment appeals to the design of highly sensitive and selective gas sensors. Primarily driven by IoT, miniaturized multisensor systems, like e-noses, are considered to address both selectivity and sensitivity issues. Although e-noses might enable discrimination between close homologs and isomers, they are required to be “trained”, i.e. to project analyte-related signals into artificial space, prior to their in-field applications. In this study, using the programmed co-precipitation method, we synthesized aluminum-doped zinc oxide (AZO) and employed it as a sensing material in an e-nose to examine the sensing performance towards close C1-C5 alcohol homologs and isomers, e.g. 1-propanol and 2-propanol, 1-butanol and isobutanol in the frame of the multisensor paradigm. For the first time, we demonstrated selective recognition of the alcohol vapors without prior training of the e-nose. This was realized by matching projections of the known analytes' “fingerprints”, used to build a chemical space, with the projections of analyte-related signals acquired using the e-nose in artificial space under machine learning algorithms. Moreover, the AZO based e-nose demonstrates a remarkable, up to 0.87, chemoresistive response to alcohol vapors, 0.9 ppm, in the mixture with air at 300 °C with a detection limit down to sub-ppb level. This opens a new avenue for the development of self-learning gas analytical systems, which might recognize new analytes whose profiles are not yet stored in their library.