Models of language processing in the human brain often emphasize the prediction of upcoming input—for example in order to explain the rapidity of language understanding. However, the precise mechanisms of prediction are still poorly understood. Forward models, which draw upon the language production system to set up expectations during comprehension, provide a promising approach in this regard. Here, we present an event-related potential (ERP) study on German Sign Language (DGS) which tested the hypotheses of a forward model perspective on prediction. Sign languages involve relatively long transition phases between one sign and the next, which should be anticipated as part of a forward model-based prediction even though they are semantically empty. Native speakers of DGS watched videos of naturally signed DGS sentences which either ended with an expected or a (semantically) unexpected sign. Unexpected signs engendered a biphasic N400—late positivity pattern. Crucially, N400 onset preceded critical sign onset and was thus clearly elicited by properties of the transition phase. The comprehension system thereby clearly anticipated modality-specific information about the realization of the predicted semantic item. These results provide strong converging support for the application of forward models in language comprehension.