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A Zebra Chip (ZC) disease detection system was developed based on hyperspectral imaging (HSI) to minimise economic losses in the New Zealand potato chip industry. Current detection methods for other than heavily diseased tubers require peeling or cutting of potato tubers. A rapid and non-destructive grading method would be ideal to remove ZC diseased potatoes at line before processing. The spectral signatures from a large population (n = 3352) of commercially sourced potatoes were collected using HSI in the spectral range of 550 nm–1700 nm. Spectral signatures of each potato (i.e. 1767 ZC infected and 1585 healthy potatoes) were extracted by segmentation and morphological operations. A calibration dataset (80% of the total population was randomly selected), with and without pre-processing, was used for modelling using the partial least squares discriminant analysis (PLS-DA). The model performance shows 92% accuracy for ZC potato identification on validation data (20% of total population). Waveband optimisation by variable importance in projection (VIP) method revealed 34 wavebands sensitive to ZC diseased potatoes. This optimum set of wavebands allowed ZC identification with 89% accuracy. The experiments demonstrate the potential of HSI for identification of ZC infected potatoes in whole tuber before processing. Efficient removal of diseased tubers would reduce processing losses and provide a potential opportunity to access export markets for intact tubers. • Hyperspectral imaging (HSI) was used to identify Zebra Chip (ZC) infected potatoes. • PLS-DA models were developed to detect ZC infected potatoes gives 92% accuracy. • Variable important scores were resulted in 34 important wavebands for ZC potatoes. • Potential of HSI for non-destructive ZC diseased potatoes detection was proven. [ABSTRACT FROM AUTHOR]