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A major goal in systems neuroscience is to characterize normal cortical dynamics. Numerous in vitro and in vivo studies demonstrated that ongoing cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. Avalanche dynamics are identified by¬ two measures (1) a power law in the size distribution of activity cascades, with an exponent of -3/2 and (2) a branching parameter of 1, which reflects a balance in the propagation of cortical activity at the border of premature termination and potential exponential blow up. Here we analyzed resting state brain activity recorded using MEG from more than 100 healthy human subjects. We identified discrete events in the MEG signal and segmented them into cascades, using multiple timescales. Cascade-size distributions were found to obey power laws. At the timescale where the branching parameter was close to the critical value of 1, the power law exponent was -3/2, in line with expectations for neuronal avalanches. This behavior was robust to scaling of the number of sensors and to coarse-graining the sensor resolution. As controls, phase-shuffled data with the same power spectrum or empty-scanner data did not exhibit neuronal avalanches. These results indicate that normal resting cortical dynamics are well described by a critical branching process. Both theory and experiments suggest that cortical networks with such critical, scale-free dynamics optimize various types of information processing. Neuronal avalanches could thus provide a biomarker for disorders in information processing, paving the way for novel quantification of normal and pathological cortical states.