Hyperspectral imaging technology is profoundly applied into the fields of agriculture, medicine and remote sensing due to its high spectral resolution, merged image-spectrum, and fast non-destructive testing. While the method used now has the defects of long-term testing period, poor efficiency and sorting asynchrony. Spectral image can identify and classify the target garbage by establishing a recognition and classification model and analyzing reflectance spectrum information based on the facts that different materials of domestic garbage, due to their different molecular structures, will absorb different wavelengths of light and the hyperspectral image can obtain the spatial information and the reflectance spectral information from different-wavelength illumination of the target garbage. Collected recyclable garbage samples of common paper, plastic and wood materials, including plastic bottles, food packaging bags, plastic toys (jewelry) pieces, disposable chopsticks, ice cream bars, wooden furniture pieces, wooden boxes, waste textbooks, advertising paper, office paper and other items, 30 in total. And cleaned and cut them to avoid the influence of sample surface stains on the sample reflectivity. Hyperspectral imaging systems were used to acquire hyperspectral images of the sample in the near-infrared (780 ～1 000 nm) formed 18 training samples and 12 test samples. Pre-processed the collected sample image by de-noising and black-and-white correction inversion of reflectivity information. Then analyzed the region of interest of training samples by principal components analysis. The characteristic band extracted were 795.815, 836.869, 885.619, 916.409, 929.239, 934.37, 957.463, 972.858, 988.253 nm; Next, matched and categorized the characteristic band of the ROI with reference spectra of the three types of garbage from the characteristic band by spectral angle mapping. The result illustrated that the classification precision of paper (A class), plastic (B class) and wood (C class) were 100%, 98% and 100% respectively and the average was 99.33%; at last, sorted the test samples by Fisher linear discrimination. The classification precision of class A, B, C were 100%, 100% and 97% respectively and the average was 99%. After a series of testing and classification by SAM and Fisher as the narrated above, the results showed that aforesaid manipulation of hyperspectral image for recyclable garbage by SAM can get more accurate results which is 99.33%. meanwhile, the research can testify that it’s feasible to apply the scheme of hyperspectral imaging to assort garbage, which is significant to methodically and automatically recycle garbage in the future.