Abstract: In this paper, a variety of targeted sensitivity analysis approaches are explored for a Bayesian Belief Network (BBN) constructed as an expert tool for enemy course of action (COA) assessment at the tactical level in a conventional mid-intensity scenario. Robustness analysis is used to measure the level to which the posterior probability of the states at the root node are affected by instantiation of individual nodes in the network. Likewise, value of information analysis and gain in belief updating are used to compare how nodes of interest affect posterior probabilities at the root node, the former measuring Shannon Entropy and the latter Kullback Distance. Finally, sensor effectiveness analysis is used to measure how the reliability of reconnaissance and surveillance (R&S) assets affects updating of belief at the root node. It was found that each of the sensitivity analysis approaches could be used to optimise allocation of R&S, to identify the commander's decision points, and to identify influential nodes for which the conditional probability tables (CPTs) should be refined. In terms of utility, it was concluded that, as in the case of the use of BBNs in the tactical COA assessment domain in general, the utility of sensitivity analysis of the BBN would be reduced in conditions of high operational tempo and myriad variables influencing tactical COA selection. Nevertheless, in a slower operational tempo environment, the benefits in refinement and utility of the BBN derived through sensitivity analysis would be significant.