We consider the problem of teconstructing the positons of a cloud of points from noisy measurements of the distances between nearby points. This algorithmic question is at the earth of numerous practical problems such as manifold learning and indoor positioning. Further, it has important connections with collaborative filtering. We study a reconstruction algorithm basedo on an appropriate semidefinite programming relaxation and prove that it has near-to-optimal robustness property.