  The available magnetic field data from the terrestrial magnetosphere, solar
wind and planetary magnetospheres exceeds over $10^6$ hours. Identifying plasma
waves in these large data sets is a time consuming and tedious process. In this
Paper, we propose a solution to this problem. We demonstrate how
Self-Organizing Maps can be used for rapid data reduction and identification of
plasma waves in large data sets. We use 72,000 fluxgate and 110,000 search coil
magnetic field power spectra from the Magnetospheric Multiscale Mission
(MMS$_1$) and show how the Self-Organizing Map sorts the power spectra into
groups based on their shape. Organizing the data in this way makes it very
straightforward to identify power spectra with similar properties and therefore
this technique greatly reduces the need for manual inspection of the data. We
suggest that Self-Organizing Maps offer a time effective and robust technique,
which can significantly accelerate the processing of magnetic field data and
discovery of new wave forms.
