In this paper, hyperspectral imaging combined with chemometrics was applied for the detection of internal defects of Korla pear. The hyperspectral images covering the spectral range of 380～1 030 nm were acquired for 60 Korla pears before, and seven consecutive days after internal damages were induced by being dropped from a distance of 30 cm. The mean spectrum were computed from region of interests (ROI) of pear in each image, and was preprocessed with wavelet transform for eliminating system noise and external disturbances, and optimizing the spectral identification region (470～963 nm). Based on the preprocessed samples, the support vector machine models were built respectively through the full and feature wavebands selected by the second derivative. The results on testing set demonstrate that both of the two approaches achieved the discrimination accuracy of 93.75%. Furthermore, F-value based method was applied for image analysis to find out the optimal waveband ratio for the visual discrimination of bruises against normal surface. Based on the optimal waveband ratio images, the selective search algorithm was utilized for segmenting bruises from the pear surface, and shows the accurate identification results. Our research revealed that the hyperspectral imaging technique for detecting bruised features in pears is feasible, which could provide a theoretical reference and basis for designing classification system of fruits in further work.