  We explore the potential of the Gaussian Mixture Model (GMM), an unsupervised
machine learning method, to identify coherent physical structures in the ISM.
The implementation we present can be used on any kind of spatially and
spectrally resolved data set. We provide a step-by-step guide to use these
models on different sources and data sets. Following the guide, we run the
models on NGC 1977, RCW 120 and RCW 49 using the [CII] 158 $\mu$m mapping
observations from the SOFIA telescope. We find that the models identified 6, 4
and 5 velocity coherent physical structures in NGC 1977, RCW 120 and RCW 49,
respectively, which are validated by analysing the observed spectra towards
these structures and by comparison to earlier findings. In this work we
demonstrate that GMM is a powerful tool that can better automate the process of
spatial and spectral analysis to interpret mapping observations.
