The ability to comprehend and produce speech after stroke depends on whether the areas of the brain that support language have been damaged. Here, we review two different ways to predict language outcome after stroke. The first depends on understanding the neural circuits that support language. This model-based approach is a challenging endeavor because language is a complex cognitive function that involves the interaction of many different brain areas. The second approach, by contrast, does not require an understanding of why a lesion impairs language; instead, predictions are made on the basis of the recovery of previous patients with the same lesion. This approach requires a database that records the speech and language capabilities of a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions. In addition, a system is required that converts an MRI scan from a new patient into a three-dimensional description of the lesion and compares this lesion against all others on the database. The outputs of this system are the longitudinal language outcomes of corresponding patients in the database. This approach will provide the patient with a range of probable recovery patterns over a variety of language measures.