Protein sequences encode information on their function and, when compared to the same proteins of different species, on their evolutionary relationships. We show in the present work how to analyze protein sequence similarities between different animal species using cellular automata imaging, which can be used to classify them according to common ancestors. Our approach consists of using the resulting states (images) from the evolution of one-dimensional cellular automata, with properly chosen evolution and coding rules, to represent a neighborhood of the active site of the lysozyme protein in each species. Hamming distances between such images are computed and then combined into a distance matrix, and from it we build a phylogenetic tree using a clustering analysis. This distance approximates animals of the same class, showing that the active site region is the most conserved for these animals. One advantage of our approach is that it only requires a neighborhood of the protein’s active site and not the whole sequence. We illustrate our approach by building a phylogenetic tree relating 19 different animal species.