  We consider computer generated configurations of quantised vortices in planar
superfluid Bose-Einstein condensates. We show that unsupervised machine
learning technology can successfully be used for classifying such vortex
configurations to identify prominent vortex phases of matter. The machine
learning approach could thus be applied for automatically classifying large
data sets of vortex configurations obtainable by experiments on two-dimensional
quantum turbulence.
