Driven by increasing customer demands, manufacturing processes now encompass increasingly intricate workflows. The industry uses computer-aided process planning to manage these complex manufacturing processes effectively. A crucial task here is to analyze product data and determine the required machining features, represented as 3D mesh geometries. However, a notable challenge arises, particularly with custom products, where the interpretation of the 3D mesh geometry varies significantly depending on the available machinery and expert preferences. This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can use a configurable synthetic data generator to encode their requirements within this framework via the training data. A machine-learning graph classification approach is used to recognize the 3D geometries of machining features in the generated data, based on to the user requirements. The system accomplishes this without requiring for data conversion into alternative formats, such as voxel or pixel representations, like other approaches are forced to.