Abstract Mount Erebus, Antarctica, has a persistent lava lake with Strombolian eruptions. Volcanic eruptions can be automatically detected with multiple methods such as cross-correlation of seismic recordings and identifying anomalies in gas emissions. We demonstrate a new method of detecting Strombolian eruptions by training a convolutional neural network to automatically categorize eruptions in infrared images obtained from the rim of the crater above the Ray lava lake atop Mount Erebus. Over 9 million images were obtained from previous research ( Peters et al., 2014a ). The infrared images were shot during a span of over 2 years; one image every 2 s when weather conditions did not hinder the electrical supply. Training was performed with infrared images of the eruptions detected through seismic cross-correlation with a stacked waveform. Eruptions detected using machine learning on infrared images from December 2013 through December 2014 correctly categorized 84% of the detections as eruptions. The remaining 16% were caused by effects from the plume confounding the neural network. Nearly all of the eruptions detected utilizing seismic cross-correlation were also categorized correctly by the neural network during periods for which image data was available. We concluded machine learning is an effective method for classifying the characteristics of Strombolian eruptions which further improves the ability to study their origins while assessing the hazards posed by volcanic eruptions.