The pathophysiology of cystic fibrosis (CF) lung disease remains incompletely understood. Novel mechanisms in the pathogenesis of CF lung disease may be discovered by studying the patterns of protein expression in bronchoalveolar lavage fluid (BALF). We used shotgun proteomics to analyze BALF samples from 8 CF and 4 control subjects. Differential protein expression between CF and control subjects was determined using spectral counting and statistical analysis. Using Gene Ontology analysis, we identified enriched biological modules and then applied network analysis to construct a protein interaction map in CF lung disease. Shotgun proteomics analysis of BALF identified hundreds of proteins whose differential enrichment or depletion robustly distinguished the CF phenotype from normal controls. Functional categorization and network analysis identified key processes, including the immune response and proteolytic activity that are known contributors to CF lung disease. Importantly, this approach also implicated abnormalities in previously unsuspected pathways, such as dysregulation of the complement system that may have critical roles in the pathogenesis of CF lung disease. By integrating shotgun proteomics with statistical and computational analyses, we have developed a promising approach to understand the pathophysiology of CF lung disease. Our approach should be applicable to a wide range of proteomics-based clinical research.