We describe three experiments combining neuroimaging and machine learning. The first experiment compares the performance of maximum likelihood and neural net classifiers for "brain reading" of fMRI data in the visual cortex. The second experiment applies the optimal classifier to measure the development of the face region in children and adolescents. While the previous experiments used block designs, the third experiment describes an event-related experiment where the classification algorithm learned something real, but not what was planned. The corroboration and validation of the classification results with brain images will be demonstrated.