Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods such as PBVI, Perseus, and HSVI, which quickly converge to an approximate solution for medium-sized problems. These algorithms improve a value function by using backup operations over a single belief point. In the simpler domain of MDP solvers, prioritizing the order of equivalent backup operations on states is well known to speed up convergence. We generalize the notion of prioritized backups to the POMDP framework, and show that the ordering of backup operations on belief points is important. We also present a new algorithm, Prioritized Value Iteration (PVI), and show empirically that it outperforms current point-based algorithms. Finally, a new empirical evaluation measure, based on the number of backups and the number of belief points, is proposed, in order to provide more accurate benchmark comparisons.