Researchers have reported successful deployments of diagnosis decision support systems based on Bayesian networks. However, the methodology for evaluating the diagnosability for such systems has not been sufficiently addressed, which consequently hinders the pace of full embracement of such systems. In this paper, we propose a methodology to analyze diagnosability for diagnosing multiple faults for systems with multi-valued discrete variables. Our analysis procedure is based on computing the configurations, p-slop MAP, for the top p percent of posterior joint distribution for faults given evidence. p-slop MAP enables us to extend diagnosability measures beyond those developed in system diagnosis literature. Our analysis results can help not only design for diagnosability, but also for developing new diagnostic procedures for systems in service.