Purpose: To determine the concordance between an Unsupervised Learning Algorithm and eye fundus color photos interpretation by a specialist for the identification of the optic disc damage. Methodology: A concordance study between an Unsupervised Learning Algorithm and a glaucoma specialist was made. The Cohen's kappa coefficient was calculated for identification of the optic disc damage in eye fundus color photos and were assessed according to Armaly´s cup/disc ratio classification. Results: The Unsupervised Learning Algorithm evaluated 689 color optic disc images classified as: healthy (no damage), mild, moderate and severe damage. A k-means classifier clustered the extracted features in four groups and obtained a Cohen's kappa coefficient of 0.037 While classifying the images in two groups: Healthy and with damage, we found a Cohen's kappa coefficient of 0.03. Conclusion: The Unsupervised Learning Algorithm for the classification of optic disc damage on color fundus photos showed a bad concordance with the one done by the glaucoma specialist, using Armaly`s cup/disc ratio classification.