The present work focuses on the development of a novel computer-based approach for tear ferning (TF) featuring. The original TF images of the recently developed five-point grading scale have been used to assign a grade for any TF image automatically. A vector characteristic (VC) representing each grade was built using the reference images. A weighted combination between features selected from textures analysis using gray level co-occurrence matrix (GLCM), power spectrum (PS) analysis and linear specificity of the image were used to build the VC of each grade. A total of 14 features from texture analysis were used. PS at different frequency points and number of line segments in each image were also used. Five features from GLCM have shown significant differences between the recently developed grading scale images which are: angular second moment at 0° and 45°, contrast, and correlation at 0° and 45°; these five features were all included in the characteristic vector. Three specific power frequencies were used in the VC because of the discrimination power. Number of line segments was also chosen because of dissimilarities between images. A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's. This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.