Comparative Graph Model is a facial recognition algorithm that takes several images of the same face, and identifies common features of that face. The algorithm looks at the closeness of features along with how many times each exists, to decide which features are the best representation of a face. The algorithm takes features from several images and condenses them down to one image, which is then used for comparison to other images. This algorithm uses a different approach than many facial recognition algorithms, which use a template to seek out facial features and make associations. CGM uses only data that is present to form associations. This makes the algorithm unsupervised, and can easily be used for other domains like object recognition. In this experiment, CGM is used in conjunction with a perceptron neural network to compare positive and negative images. Ten training images were condensed to one image via CGM, and compared against one positive image (the same person), and one negative image (a different person). In the experiment, each trial was run for 100 iterations. The algorithm recognized the same person 71% correctly, did not recognize a different person 79% correctly, with an overall correctness of 61%. Using an unsupervised algorithm is beneficial across many disciplines, especially where a pattern can be too hard to find or represent. The algorithm does not need to know what it is looking for to make associations, which makes it a good candidate for finding complex patterns and associations in data.