In the United States, cultivated blueberries are second only to strawberries as one of the most important berries. In Georgia, the blueberry industry has grown by 170% in economic value between 2000 and 2005 and has become Georgia’s most important fruit crop with a total farm gate value exceeding $75 million. However, blueberries are also a highly perishable fruit, and more than 20% of the berries are typically lost before they get to consumers. Furthermore, physical damage renders blueberry fruit more susceptible to certain fungal diseases such as gray mold (Botrytis cinerea), anthracnose (Collecotrichum spp.), and Alternaria rot (Alternaria spp.). A conducting polymer gas sensor array was evaluated for detecting and classifying blueberry fruit infected with these three common fungal postharvest pathogens. Samples of rabbiteye blueberries (Vaccinium virgatum cv. Brightwell) were inoculated individually with one of the three pathogens or left uninoculated, and volatiles emanating from the fruit were assessed using a gas sensor array 6-10 days after inoculation in two separate experiments. Principal component analysis revealed four distinct groups corresponding to the four inoculation treatments. A hierarchical cluster analysis indicated two super-clusters, i.e., control cluster (non-inoculated fruit) vs. pathogen cluster (inoculated fruit). Within the pathogen cluster, fruit infected by B. cinerea and Alternaria sp. were more similar to each other than to fruit infected by C. gloeosporioides. A linear Bayesian classifier achieved 90% overall correct classification for combined data from two experiments. Gas chromatography-mass spectrometry identified six compounds [styrene, 1-methyl-2-(1-methylethyl) benzene, eucalyptol, undecane, 5-methyl-2-(1-methylethyl)-2-cyclohexen-1-one, and thujopsene] that contributed the most in distinguishing differences in the volatiles emanating from the fruit due to infection. This study underscores the potential feasibility of using a gas sensor array for blueberry postharvest quality assessment and fungal disease detection.