The rise of social media and its resultant impact on brand management has become a critical factor in guarding the reputation of the firm. Consumer-generated content has the potential to spread rapidly over social networks and the implications are that advertising as traditionally used by brand managers, now offers little control over the communication message. Brand managers need a better tool to gauge the changing mood in social media conversations. The purpose of this paper is to suggest a powerful method, Chernoff Faces, to compare six Sauternes wine brands based on social conversation measurement. This study describes a source of data relating to wine brand visibility in social media, and then presents a simple yet powerful graphical tool for portraying this information. This tool facilitates the communication, understanding, and assimilation of the relevant information. The findings of this paper are presented in six social media wine faces. Facial features are allocated to eyes, facial line, hair density and others to reflect “Social Mention” data measuring brand strength, positive and negative sentiment and related elements such passion for the brand. A brief subjective interpretation of the differences between the wine brands offers a match between Chernoff faces representation and historical data on the brands being compared. The paper has some limitations related to the dynamic nature of social media. This study provides more of a snapshot in time rather than an ultimate set of results. Future research could be done by closely monitoring the results for a set of brands over a period. A new option to overcome this by using longitudinal data is offered as a option in future research. Since social media are multi-dimensional and attempts to understand conversations it requires tracking different measures simultaneously. It is important to find the best way to portray and communicate this data so that wine marketing decision makers can quickly and easily compare changes in brand images. Using faces to accomplish this is an easy and novel way compared to more demanding multidimensional scaling techniques.