Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use pointbased value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy.We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.