In the present study, online measures of letter identification were used to test computational models of letter perception. Event-related potentials (ERPs) were recorded to letters and pseudo-letters revealing a transition from feature analysis to letter identification in the 100-200 ms time window. Measures indexing this transition were then computed at the level of individual letters. Simulations with several versions of an interactive-activation model of letter perception were fitted with these item-level ERP measures. The results are in favor of a model of letter perception with feedforward excitatory connections from the feature to the letter levels, lateral inhibition at the letter level, and excitatory feedback from the letter to the feature levels.