The objective of this study was to develop prediction models for the severity of chilling injury in kimchi cabbages (KCs) using short-wave infrared hyperspectral imaging (HSI) combined with supervised classifications (partial least square discriminant analysis, PLS-DA; and artificial neural network, ANN) at early stages. Classifications were implemented for the two-class (i.e., normal and chilled), three-class (i.e., normal, semi, and severe) and four-class (i.e., normal, slight, moderate, and severe) classifications at full and selected optimal wavelengths (associated with the C–H stretch of sugar and cellulose). The PLS-DA classifier with Savitzky-golay (SG) preprocessing showed improved average classification accuracy of 0.912 and 0.896 for three-class and four-class classification developed with optimal wavelength, respectively. The best classification results based on ANN using optimal wavelengths and SG achieved overall accuracies above 0.924 for all-class classifications. ANN models have higher potential and are simplified to predict chilling damage characteristics of KCs compared to PLS-DA models. By visualizing injured areas, we showed the association between the internal changes as detected by HSI. These results demonstrate the capability of HSI and the central role of chemometrics and neural networks in developing efficient models for prediction of chilling damage properties of KCs.