The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak, a phenomenon known as the clinico-radiological paradox. Here, we investigated to which degree it is possible to predict individual disease profiles from conventional magnetic resonance imaging (MRI) using multivariate analysis algorithms. Specifically, we conducted cross-validated canonical correlation analyses to investigate the predictive information contained in conventional MRI data of 40 MS patients for the following clinical parameters: disease duration, motor disability (9-Hole Peg Test, Timed 25-Foot Walk Test), cognitive dysfunction (Paced Auditory Serial Addition Test), and the expanded disability status scale (EDSS). It turned out that the information in the spatial patterning of MRI data predicted the clinical scores with correlations of up to 0.80 (p < 10(-9)). Maximal predictive information for disease duration was identified in the precuneus and somatosensory cortex. Areas in the precuneus and precentral gyrus were maximally informative for motor disability. Cognitive dysfunction could best be predicted using data from the angular gyrus and superior parietal lobe. For EDSS, the inferior frontal gyrus was maximally informative. In conclusion, conventional MRI is highly predictive of clinical disability in MS when pattern-based algorithms are used for prediction. Thus, the so-called clinico-radiological paradox is not apparent when using suitable analysis techniques.