Abstract: Feather and hemp are two kinds of foreign fibers frequently found in cotton, which are difficult to identify using existing image processing methods. A novel image processing method was proposed to classify the two impurities in lint. Three color and five texture features were extracted for these impurities from machine-acquired images of lint samples. An agglomerate hierarchical cluster analysis was conducted, and dimensionality reduction was performed to determine the optimal number of color and texture features. Such agglomerate hierarchical cluster analysis resulted in rates of correct identification of 94% for feather and 95% for hemp. The optimal combination was obtained with six features (color coordinates R, G, B and energy, entropy, and moment of inertia) in the hierarchical cluster analysis. The research can provide a reference for the correct recognition in cotton foreign fibers.