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We propose a stochastic analytical hierarchy process (SAHP) method.Sustainable biomass crop selection has economic, environmental, and social dimensions.Switchgrass is found to be the most sustainable crop type for biofuel production.Wheat and corn get high scores if economic factor is emphasized in decision making.The proposed SAHP model can utilize imprecise expert opinions in decision making. Selecting the most sustainable biomass crop type for biofuel production is a multi-criteria decision-making (MCDM) problem involving various conflicting criteria. In this paper, we propose a unique stochastic analytical hierarchy process (AHP) that can handle uncertain information and identify weights of criteria in the MCDM problem. By utilizing the beta distribution and approximating its median, we convert various types of expert evaluations including imprecise values into crisp values. We ensure consistency in each evaluation matrix before aggregating expert judgments. We then demonstrate use of the model by applying it to sustainable biomass crop selection. In order to define a comprehensive list of the selection criteria, we utilize the existing literature and opinions of experts including farmers, government specialists from the U.S. Department of Agriculture (USDA), and faculty members in the areas of biomass and bioenergy. The evaluation model includes three main sustainability criteria defined as economic, environmental, and social aspects associated with a total of 16 sub-criteria. We apply the proposed model to biomass alternatives including switchgrass, Miscanthus, sugarcane, corn, and wheat in Kansas. Results show the weights of economic, environmental, and social aspects to be 0.59, 0.26, and 0.15, respectively. The sensitivity analysis indicates that the score of switchgrass increases if environmental criteria are emphasized. On the other hand, wheat and corn become more favorable than other alternatives if priority is given to economic factors. The most sustainable biomass sources in different regions can be determined by applying the presented selection hierarchy. The proposed stochastic AHP methodology can also be utilized for other complex multi-criteria decision-making problems with uncertain information and multiple stakeholders.