Pattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external stimuli or internal brain states. When applied to the analysis of electroencephalography (EEG) or magnetoencephalography (MEG) data, a choice needs to be made on how the input features to the algorithm are obtained from the signal amplitudes measured at the various channels. In this article, we consider six types of pattern analyses deriving from the combination of three types of feature selection in the temporal domain (predefined windows, shifting window, whole trial) with two approaches to handle the channel dimension (channel wise, multi-channel). We combined these different types of analyses with a Gaussian Naive Bayes classifier and analyzed a multi-subject EEG data set from a study aimed at understanding the task dependence of the cortical mechanisms for encoding speaker's identity and speech content (vowels) from short speech utterances (Bonte, Valente, & Formisano, 2009). Outcomes of the analyses showed that different grouping of available features helps highlighting complementary (i.e. temporal, topographic) aspects of information content in the data. A shifting window/multi-channel approach proved especially valuable in tracing both the early build up of neural information reflecting speaker or vowel identity and the late and task-dependent maintenance of relevant information reflecting the performance of a working memory task. Because it exploits the high temporal resolution of EEG (and MEG), such a shifting window approach with sequential multi-channel classifications seems the most appropriate choice for tracing the temporal profile of neural information processing.