All sufficiently-complex engineered systems require oversight by committees of technical experts. Decision-making by these expert committees is poorly understood. A number of social dynamics might impact the sharing of information among expert specialists. Some of these could be beneficial to the decision process but some could lead to decisions that are not well-informed by all of the specialties represented. This research presents a quantitative empirical methodology for the study of technical expert committees based upon computational linguistic analysis of meeting transcripts. The Food and Drug Administration advisory panels are used as a case study. Output results include meaningful social network data that might potentially be used to gain insight into how the social dynamics of expertise interact with technical device attributes, ultimately leading to a committee decision.