We investigate the effects of two types of relationship between the words of a sentence or text – predictability and semantic similarity – by reanalysing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from studies in which participants comprehend naturalistic stimuli. Each content word's predictability given previous words is quantified by a probabilistic language model, and semantic similarity to previous words is quantified by a distributional semantics model. Brain activity time-locked to each word is regressed on the two model-derived measures. Results show that predictability and semantic similarity have near identical N400 effects but are dissociated in the fMRI data, with word predictability related to activity in, among others, the visual word-form area, and semantic similarity related to activity in areas associated with the semantic network. This indicates that both predictability and similarity play a role during natural language comprehension and modulate distinct cortical regions.