Railway traffic is a set of interrelated processes that are centrally controlled. Despite optimized train schedules, train dispatchers still take ad-hoc decisions on the scheduling of trains in the context of unplanned events. Train orders are swapped, train crossings on single-tracks are moved, or trains are cancelled to minimize the disruption in the schedule. The actual scheduling of trains, as decided by dispatchers and observed through the movement of trains across stations, is then registered in railway traffic control logs. Using this data that contains information on the tacit knowledge of dispatchers can help to evaluate strategies for dealing with disruptions, which have not been subject to upfront planning. This paper proposes to use process mining methods, which are commonly applied in the context of business processes, to expose the hidden process of how the train traffic was actually dispatched. Different variants of dispatching are juxtaposed with the total delay in the railway system to visually explore the dispatching strategies taken. The technique has been implemented as a prototype and validated on a large dataset of real-life traffic in the Norwegian railway system.