We use the susceptible-infected-recovered (SIR) model for disease spread over a network, and empirically study how well various centrality measures perform at identifying which nodes in a network will be the best spreaders of disease on 10 real-world networks. We find that the relative performance of degree, shell number and other centrality measures can be sensitive to B, the probability that an infected node will transmit the disease to a susceptible node. We also find that eigenvector centrality performs very well in general for values of B above the epidemic threshold.