In the tender process, the customer publishes a request for tender (RFT) document containing a large list of contractly binding requirements. Suppliers need to process all of them and come up with solutions for each requirement. This thesis is written in cooperation with an industry partner on the supplier side. Since not a single person can answer all requirements, these are further assigned to responsible experts. This split is performed based on roles within the project, such as project management or technical experts for some of the companys products. Within this thesis, such a role is abstractly called subsystem. This assignment is done manually by a single person, making this task tedious and time-consuming. To support the partner, a machine learning approach is developed to automatically assign requirements to subsystems. In a literature review, suitable machine learning methods are identified, which are then compared in a benchmark to find the best configuration for each of four selected subsystems. These configurations are then checked upon generalization by evaluating them on five additional subsystems. The reasons for false classification are then identified in an interview with the person, who is currently in charge with the assignment.