Business Process Model and Notation (BPMN) is a standard business process modelling language that allows users to describe a set of structured tasks, which results in a service or product. Before running a BPMN process, the user often has no clear idea of the probability of executing some task or specific combination of tasks. This is, however, of prime importance for adjusting resources associated with tasks and thus optimising costs. In this paper, we define an approach to perform probabilistic model checking of BPMN models at runtime. To do so, we first transform the BPMN model into a Labelled Transition System (LTS). Then, by analysing the execution traces obtained when running multiple instances of the process, we can compute the probability of executing each transition in the LTS model, and thus generate a Probabilistic Transition System (PTS). Finally, we perform probabilistic model checking for verifying that the PTS model satisfies a given probabilistic property. This verification loop is applied periodically to update the results according to the execution of the process instances. All these steps are implemented in a tool chain, which was applied successfully to several realistic BPMN processes.