Working memory (WM) plays a central role in cognition, prompting neuroscientists to investigate its functional and structural substrates. The WM dynamic recruits large-scale frequency-specific brain networks that unfold over a few milliseconds – this complexity challenges traditional neuroimaging analyses. In this study, we unravel the WM network dynamics in an unsupervised, data-driven way, applying the time delay embedded-hidden Markov model (TDE-HMM). We acquired MEG data from 38 healthy subjects performing an n-back working memory task. The TDE-HMM model inferred four task-specific states with each unique temporal (activation), spectral (phase-coherence connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal state performs executive functions, an alpha temporo-occipital state maintains the information, and a broad-band and spatially complex state with an M300 temporal profile leads the retrieval process and motor response. The HMM states can be straightforwardly interpreted within the neuropsychological multi-component model of WM, significantly improving the comprehensive description of WM.HighlightsWorking memory recruits different frequency-specific brain networks that wax and wane at a millisecond scale.Through the time-delay embedded hidden (TDE-HMM) we are able to extract data-driven functional networks with unique spatial, spectral, and temporal profiles.We demonstrate the existence of four task-specific brain networks that can be interpreted within the well-known Baddeley’s multicomponent model of working memory.This novel WM description unveils new features that will lead to a more in-depth characterization of cognitive processes in MEG data.