Network distance, measured as round-trip latency between hosts, is important for the performance of many Internet applications. For example, nearest server selection and proximity routing in peer-to-peer networks rely on the ability to select nodes based on inter-host latencies. This paper presents PCoord, a decentralized network coordinate system for Internet distance prediction. In PCoord, the network is modeled as a D-dimensional geometric space; each host computes its coordinates in this geometric space to characterize its network location based on a small number of peer-to-peer network measurements. The goal is to embed hosts in the geometric space so that the Euclidean distance between two hosts? coordinates accurately predicts their actual inter-host network latency. PCoord constructs network coordinates in a fully decentralized fashion. We present several mechanisms in PCoord to stabilize the system convergence. Our simulation results using real Internet measurements suggest that, even under an extremely challenging flash-crowd scenario where 1740 hosts simultaneously join the system, PCoord with a 5-dimensional Euclidean model is able to converge to 11% median prediction error in 10 coordinate updates per host on average.