Surge-type glaciers are present in many cold environments in the world. These glaciers experience a dramatic increase in velocity over short time periods, the surge, followed by an extended period of slow movement, the quiescence. The detailed processes that control this intermittent behaviour remain elusive. We develop a machine learning framework to classify surge-type glaciers, based on their location, exposure, geometry, surface mass balance and runoff. We apply this approach to the Svalbard archipelago, a region with a relatively homogeneous climate. We compare the performance of logistic regression, random forest, and extreme gradient boosting (XGBoost) machine learning models that we apply to a newly combined database of glaciers in Svalbard. Based on the most accurate model, XGBoost, we compute surge probabilities along glacier centerlines and quantify the relative importance of several controlling features. Results show that the surface and bed slopes, ice thickness, glacier width, surface mass balance and runoff along glacier centerlines are the most significant features explaining surge probability for glaciers in Svalbard. A thicker and wider glacier with a low surface slope has a higher probability to be classified as surge-type, which is in good agreement with the existing theories of surging. Finally, we build a probability map of surge-type glaciers in Svalbard. Our methodology could be extended to classify surge-type glaciers in other areas of the world.