Committees often decide on proposals that are inherently multidimensional: e.g., whether a medical drug up for approval is safe and effective, or a new investment’s profitability and risk. In this paper, we first provide a model of multidimensional learning in committees where members may collectively — and strategically — learn about different dimensions during deliberation, and that information is germane to the final decision. We show that the multidimensional nature of deliberations, including the spillovers of information and the preferences across dimensions, affects how committees learn and decide. Then, we use data on rosters, minutes, and the text from transcripts from FDA Advisory Committees, where experts provide advice for the approval of new drugs, biological products and medical devices in the United States, and show that we can statistically identify and consistently estimate this model. Finally, we discuss how alternative institutional designs, including changing committee compositions and information structures, affect policy decisions.