Applications on inference of biological networks have raised a strong interest on the problem of graph estimation in high-dimensional Gaussian graphical model. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting and its risk is controlled by a non-asymptotic oracle-like inequality. A nice behavior on numerical experiments corroborates these theoretical results. The procedure is implemented in the R-package GGMselect available on http://w3.jouy.inra.fr/unites/miaj/public/logiciels/GGMselect/