An important problem in realizing personalized medicine is the development of methods for identifying disease subtypes using quantitative proteomics. Recently we found that bronchoalveolar lavage (BAL) cytokine patterns contain information about dynamic lung responsiveness. In this study, we examined physiological data from 1,048 subjects enrolled in the US Severe Asthma Research Program (SARP) to identify four largely separable, quantitative intermediate phenotypes. Upper extremes in the study population were identified for eosinophil- or neutrophil-predominant inflammation, bronchodilation in response to albuterol treatment, or methacholine sensitivity. We evaluated four different statistical ("machine") learning methods to predict each intermediate phenotype using BAL A-cytokine measurements on a 76 subject subset. Comparison of these models using area under the ROC curve and overall classification accuracy indicated that logistic regression and multivariate adaptive regression splines produced the most accurate methods to predict intermediate asthma phenotypes. These robust classification methods will aid future translational studies in asthma targeted at specific intermediate phenotypes.