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Yellow feather chicken is a kind of traditional poultry in China. In recent years, labor shortages and diseases are two factors that affect production and health of yellow feather chicken. Using machine vision instead of manual monitoring of chickens and rapid identification of diseased chickens is a new research direction to solve the plight of poultry breeding. The symptoms of a diseased chicken mainly occurred in the eyes and comb which were mainly in the head. A method to recognize diseased chickens based on the head features was proposed. At first, totally 500 images of the healthy chickens and 236 images of the diseased ones were separately captured from the videos taken in the actual feeding environment composed the graphic base for the experiment. From each image, 10 chicken areas with 10 pixels×10 pixels, three comb areas with 5 pixels×5 pixels and 10 background areas with 10 pixels×10 pixels were selected to form the sample sets. The R,G,B and H component values of each pixel in the sample sets were obtained to get color difference threshold of the background and H threshold of the comb and chicken. Segmentation of the yellow feather chicken could be completed by using the color difference threshold for removing the background and the H thresholds for the object regions. Combs and eyes and their contours were also obtained in HSV color model by the H threshold. Then distance d1 between any two points on the different contours of the combs was calculated. The points if d1 was less than the maximal side of the external rectangle of each comb were connected to make the two combs to be a whole. The external rectangle of the connected combs included the chicken head. A correcting method was to extend this external rectangle to both sides until the width of the rectangle was larger than the height to improve the integrity of the chicken head recognition. Co-occurrence matrix of H component was calculated from the comb to get the color feature and the texture feature including ASM，COR，IDM，Ent 6 geometry features including A，P，R，E，C and A/P were computed from the eye contour. All these 10 features formed characteristic vector of the chicken head. Average recognition accuracy (ARA) was adopted to select the best feature set (ASM，Ent，E and A/P). At last, SVM was trained by 420 healthy images and 200 diseased images, and then tested by 80 healthy chicken images and 36 diseased chicken images. Accuracy of the identification of diseased ones was 92.5%. The test results indicated that the proposed method was feasible and valuable to identify diseased chickens by head features using machine vision.