Reordering poses one of the greatest challenges in Statistical Machine Translation research as the key contextual information may well be beyond the confine of translation units. We present the "Anchor Graph" (AG) model where we use a graph structure to model global contextual information that is crucial for reordering. The key ingredient of our AG model is the edges that capture the relationship between the reordering around a set of selected translation units, which we refer to as anchors. As the edges link anchors that may span multiple translation units at decoding time, our AG model effectively encodes global contextual information that is previously absent. We integrate our proposed model into a state-of-the-art translation system and demonstrate the efficacy of our proposal in a large-scale Chinese-to-English translation task.