Abstract: Strawberry postharvest diseases usually cause heavy losses in storage. Electronic nose (PEN3) containing an array of 10 different metal oxide sensors was used to detect and classify three kinds of common postharvest fungal diseases of strawberry fruit: Botrytis sp. (BC), Penicillium sp. (PE) and Rhizopus sp. (RH) in this paper. Ripe strawberry fruits were inoculated individually with the three pathogens and non-inoculated samples with sterile water treatment as control. Volatile compounds emanating from strawberry fruit were assessed using PEN3 every two days after inoculation. On the second day after invocation, the principal component analysis (PCA) of volatile profiles can clearly distinguish between normal and infection strawberry fruit; Furthermore, it can discriminate three groups of strawberry fruit with different pathogenic bacteria. Multivariate analysis of variance (MANOVA) was conducted on the e-nose sensors’ response to the strawberry fruit with different treatment on the second day and the volatile compounds were analyzed by gas chromatography mass spectrometry (GC-MS). The results confirmed that the four treatments were significantly different (P < 0.05). A Fisher classifier was set up and achieved classification accuracy of 100%, 93.3%, 86.7% and 100% for treatment of BC, PE, RH and CK, respectively. Loading analysis and GC-MS were used to characterize volatile compounds emanated from the four groups of strawberry fruit, hydrocarbons and esters were identified as contributing mostly in distinguishing differences in the volatiles emanating from the fruit due to infection. This study showed the potential feasibility for the rapidly nondestructive detection and monitoring of quality and fungal disease infection of strawberry fruits during postharvest storage using an electronic nose.