The increase of product variants and their frequent change result in new challenges for manufacturing companies. Companies have to find a way to efficiently manage the production of all variants by having rapidly adaptable manufacturing resources. However, in order to utilize the full potential of these types of resources, a machine interpretable capability model is required. Such a model can facilitate the production planning and reconfiguration process, as production requests can be quickly analyzed by the usage of the model. In addition, the model can be utilized to generate new configurations, which match the changing requirements. Accordingly, this contribution presents an approach for the modeling of manufacturing resource capabilities. The approach aims at providing an efficient basis for the matching of production requests and resource capabilities. Furthermore, the capability model should enable a change analyses for the adaptation of the resources, in order to quickly adjust them to the changing requirements. The proposed capability model has been implemented in AutomationML and XML, which allows a machine-interpretable representation of the model in a neutral data format. The applicability of the approach is demonstrated by a case study of an industrial high-voltage testing machine.