Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a semi-automatic framework for mining ERP data, which includes the following steps: PCA decomposition, extraction of summary metrics, unsupervised learning (clustering) of patterns, and supervised learning, i.e. discovery, of classification rules. Results show good correspondence between rules that emerge from decision tree classifiers and rules that were independently derived by domain experts. In addition, data mining results suggested ways in which expert-defined rules might be refined to improve pattern representation and classification results.