We describe the use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart. We used direct kernel methods to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, we introduced Direct Kernel based Self-Organizing Maps. For supervised learning we used Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyperparameters for these methods were tuned on a validation subset of the training data before testing. We also investigated the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms and experimented with variable selection by filtering. The results, similar for all three methods, were encouraging, exceeding the quality of classification achieved by the trained experts.