Abstract: The volatile organic compounds (VOCs) of three kinds of fruit beer, namely pineapple, apple and cranberry beers were investigated, and the VOCs data was discriminated and classified by using principal component analysis (PCA) method. The results showed that a total of 35 effective characteristic ionic peaks of the VOCs mainly ethanol, isoamyl acetate, ethyl hexanoate, isoamyl alcohol, ethyl butyrate, benzaldehyde, and ethyl octanoate were selected by the two-dimensional mode of data visualization based on the SH-GC-IMS fingerprint, which could be used to characterize the flavor differences among the selected fruit beers. Discrimination and classification of the three kinds of fruit beer was effectively performed by using PCA method with good dispersion, and all groups had corresponding attribution areas in the PCA map and the cumulative contribution rate of the first two PCs was 98%, which could be used to distinguish the aroma characteristics of different fruit beer products. Anyway, the results offer a new method for process control, product traceability, brand identification and protection in fruit beer industry.