Abstract: Different types of trash in ginned cotton lint seriously affect the grade of textile materials and the quality of the final woven product. In this work, hyper-spectral imaging operated in reflectance mode in a spectral region from 422 to 982 nm was studied to detect light color, white, colorless and fine foreign materials on the surface of the ginned cotton. The foreign materials include gray, white and transparent polypropylene fiber, black human hair, black and white pig hair, black and transparent PE mulching film. Traditional methods of dimension reduction of hyper-spectral image were applied to obtain the potential images. Image enhancement of medial filter and edge detection based on Sobel operator were initially selected for segmentation of the potential images. Subsequently, 'dilation' and 'erosion' morphological operation were carried out to separate the targets from the background. An area filter was finally used to remove noise and small components of suspected non-target in binary images. The overall recognition accuracies for all foreign materials in the training and independent test sets were up to 73.2% and 75.3%, respectively. More than 93% of recognition rate for gray polypropylene fiber and black hairs were achieved. No fewer than 80% of white polypropylene fibers were accurately segmented. Although the recognition rate for transparent polypropylene fibers, transparent PE mulching film, and white pig hair were relatively low, our work starts a new attempt to detect these foreign materials of ginned cotton. The study has shown that the hyper-spectral imaging system can provide more subtle spatial and spectral information for segmentation and recognition of some foreign materials of ginned cotton, like white polypropylene fibers.