Hyperspectral imaging, an emerging analytical technology for quality and safety inspections of agricultural and sideline products, combines the advantages of digital image or computer vision with spectroscopy technology in the whole system. Hyperspectral imaging can simultaneously acquire both spatial and spectral information, which deliver chemical, structural and functional information from the samples. In this work, hyperspectral imaging technology was applied to determine a classifier that can be used for nondestructive defection of the defective features in “No.9 of Zhongyou” nectarine fruit. There were 400 samples from a nectarine planting garden in the Wanan Village in Yuncheng City of Shanxi Province, China, including: crack(50), peel spots(50), malformation(50), hidden damage(50) and normal(200) samples. Hyperspectral imaging technology covered the range of 420~1000nm was employed to detect defects (crack, peel spots, malformation and hidden damage) of nectarine fruit. 400 RGB images were acquired through a total of 400 samples, which included four types of defective features and sound samples. After acquiring hyperspectral images of nectarine fruits, the spectral data were extracted from region of interest (ROI). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (280) and testing set (120). First of all, based on the calculation of partial least squares regression (PLSR), 10 wavelengths at 497nm, 534nm, 657nm, 677nm, 696nm, 709nm, 745nm, 823nm, 868nm and 943nm were selected as the optimal sensitive wavelengths (SWs), respectively. Subsequently, the image of the 876nm wavelength was named as the feature image, then principal component analysis (PCA), mask process, “Sobel” edge detector and “region grow” algorithm were carried out among defective and normal nectarines to extract the defective region. Moreover, ten principal components (PCs) were selected based on PCA, and seven textural feature variables (mean, contrast, correlation, energy, homogeneity and entropy) were extracted by using gray level cooccurrence matrix (GLCM), respectively. Finally, the ability of hyperspectral imaging technique was tested by using the extreme learning machine (ELM) models. The ELM classification model was built based on the combination of PCs and textural features. The results show the correct discrimination accuracy of defective samples was 91.67%, and the correct discrimination accuracy of normal samples was 100%. The research revealed that the hyperspectral imaging technique is a promising tool for detecting defective features in nectarine, which could provide a theoretical reference and basis for designing classification system of fruits in further work.