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To predict the immediate evolution of a plasma system, one needs to identifythe nature of the dominant instability. In this work, a neural network isemployed to address a non-binary classification problem of instabilities inastrophysical jets, whose natural oscillations and instabilities are quantifiedwith the magnetohydrodynamic spectroscopy code Legolas. The trained modelsexhibit reliable performance in the identification of the two instability typessupported by these jets. To improve the neural network aided classificationprocess, techniques for training data augmentation and refinement ofpredictions for general eigenproblems are discussed.

Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas