The goal of this report is to determine the best feature vector visualization method for a visual understanding of the similarity between instances within a tabular dataset. Image-based datasets are very powerful and the property of the data being images is used by researchers to create impactful visualization tools. Tabular data is unable to utilize these visualization tools because there is not an effective way to view a feature vector of several numbers. This research compares two existing visualization techniques – Star Glyphs and Chernoff Faces – against a proposed solution – Reticle Glyphs – to determine the best technique for visualizing similarity. Visualizing similarity is important to help enhance the visualization tool I created this past term. To test and compare these methods a survey was conducted, and interviews were held with Machine Learning Scientists within Layer6. The results of the study show that the proposed solution serves as the most accurate visual representation of similarity within the limitation of the research. Further research must be conducted to determine why the Reticle Glyphs does not garner the highest confidence from its users.