We present a new framework for the diagnosis of schizophrenia based on the spectro-temporal patterns selected by a support vector machine from multichannel magnetoencephalogram (MEG) recordings in a verbal working memory task. In the experimental paradigm, five letters appearing sequentially on a screen were memorized by subjects. The letters constituted a word in one condition and a pronounceable nonword in the other. Power changes were extracted as features in frequency subbands of 248 channel MEG data to form a rich feature dictionary. A support vector machine has been used to select a small subset of features with recursive feature elimination technique (SVM-RFE) and the reduced subset was used for classification. We note that the discrimination between patients and controls in the word condition was higher than in the non-word condition (91.8% vs 83.8%). Furthermore, in the word condition, the most discriminant patterns were extracted in delta (1-4 Hz), theta (4-8Hz) and alpha (12-16 Hz) frequency bands. We note that these features were located around the left frontal, left temporal and occipital areas, respectively. Our results indicate that the proposed approach can quantify discriminative neural patterns associated to a functional task in spatial, spectral and temporal domain. Moreover these features provide interpretable information to the medical expert about physiological basis of the illness and can be effectively used as a biometric marker to recognize schizophrenia in clinical practice.