The objective is to combine simultaneous neurophysiologic signals from team members to develop pattern categories, called neurophysiologic synchronies (NS) that can be related to the second-by-second activities of teams. Neurophysiologic synchronies are a low level data stream that can be collected and analyzed in real time and in realistic settings. If the expression of different NS patterns is sensitive to changes in the behavior of teams over time they may be a useful tool for studying team cognition. EEG-derived measures of engagement from team members were normalized and pattern classified by self-organizing artificial neural networks and hidden Markov models. The temporal expression of these patterns was mapped onto team events. Across multiple teamwork tasks NS expression was shown to be non-random and sensitive to changes in the task and the activities of team members. These studies suggest that neurophysiologic indicators measured by EEG may be useful for studying team behavior not only at the milliseconds level, but at more extended time frames.