Decision making in operational planning is increasingly affected by conflicting interests of different stakeholders such as subcontractors, customers, or strategic partners. Addressing this, automated negotiation is a well-suited mechanism to mediate between stakeholders and search for jointly beneficial agreements. However, the outcome of a negotiation is strongly dependent on the applied negotiation protocol defining the rules of encounter. Although protocol design is well discussed in literature, the question on which protocol should be selected for a given scenario is little regarded so far. Since negotiation problems and protocols are very diverse, the protocol choice itself is a challenging task. In this study, we propose a decision support system for negotiation protocol selection (DSS-NPS) that is based on a machine learning approach – an artificial neural network (ANN). Besides presenting and discussing the system, we, furthermore, evaluate the design artifact in elaborate computational experiments that take place in an intercompany machine scheduling environment. Our findings indicate that the proposed decision support system is able to improve the outcome of negotiations by finding adequate protocols dynamically on the basis of the underlying negotiation problem characteristics.