Background: Breath volatile organic compounds (VOCs) may be Useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of breath VOCs in patients with asthma and in clinically relevant disease phenotypes. Methods: Breath samples were analysed by gas chromatography-mass spectrometry. The Asthma Control Questionnaire was completed, together with lung function and induced sputum cell counts. Breath data were reduced to principal components, and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis, sputum inflammatory cell profile and asthma control. Results: The authors recruited 35 patients with asthma and 23 matched controls. A model derived from 15 VOCs classified patients with asthma with an accuracy of 86%, and positive and negative predictive values of 0.85 and 0.89, respectively. Models also classified patients with asthma based on the following phenotypes: sputum (obtained in 18 patients with asthma) eosinophilia ≥2% area under the receiver operating characteristics (AUROC) curve 0.98, neutrophilia ≥40% AUROC 0.90 and uncontrolled asthma (Asthma Control Questionnaire ≥1) AUROC 0.96. Conclusions: Detection of characteristic breath VOC profiles could classify patients with asthma versus controls, and clinically relevant disease phenotypes based on sputum inflammatory profile and asthma control. Prospective validation of these models may lead to clinical application of non-invasive breath profiling in asthma.