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This chapter provides a tutorial style guide to analyzing electroencephalogram (EEG) data contingent on feature-based attentional selection. It is targeted at researchers that currently investigate attentional processes using univariate methods but consider moving to multivariate analyses. The chapter starts by providing examples of classical univariate analysis, in which the EEG signal occurring ipsilateral to the target is subtracted from the signal that occurs in a contralateral electrode (i.e., the classical N2pc, an interhemispheric posterior negativity emerging around 180–200 ms). Next, it shows how the same type of information can also be identified using multivariate pattern analysis (MVPA). MVPA does not restrict one to contrast attentional selection in opposite hemifields but also allows one to assess attentional selection on the vertical meridian, or even within a quadrant of the visual field, opening up new …