How quickly can information about the neural response to a visual stimulus be detected in the hemodynamic response measured using fMRI? Multi-voxel pattern analysis (MVPA) uses pattern classification to detect subtle stimulus-specific information from patterns of responses among voxels, including information that cannot be detected in the average response across a given brain region. Here we use MVPA in combination with rapid temporal sampling of the fMRI signal to investigate the temporal evolution of classification accuracy and its relationship to the average regional hemodynamic response. In primary visual cortex (V1) stimulus information can be detected in the pattern of voxel responses more than a second before the average hemodynamic response of V1 deviates from baseline, and classification accuracy peaks before the peak of the average hemodynamic response. Both of these effects are restricted to early visual cortex, with higher level areas showing no difference or, in some cases, the opposite temporal relationship. These results have methodological implications for fMRI studies using MVPA because they demonstrate that information can be decoded from hemodynamic activity more quickly than previously assumed.