The N400 ERP component is widely used, but the cognitive functions underlying N400 amplitudes are still unclear. Recent simulations with a model of word meaning suggest that N400 amplitudes reflect implicit semantic prediction error. Here, we extend these simulations to sentence comprehension, using a model of sentence processing to simulate a number of N400 effects obtained in empirical research. In the model, sequentially incoming words update a representation capturing probabilities of elements of sentence meaning, not only reflecting the constituents presented so far, but also the model’s best guess at all features of the sentence meaning based on the statistical regularities in its environment. Across a series of simulations, the update of the predictive representation of sentence meaning consistently patterned with N400 amplitudes, in line with the idea that N400 amplitudes reflect semantic surprise as the change in the probability distribution over semantic features in an integrated representation of meaning.