The analysis of multichannel signals (MCS) has received a great deal of attention in the past few years. Modeling MCS requires depicting not only the temporal correlations within each single-channel signal (SCS) but also the interdependencies between marginal signals. The vector autoregressive (VAR) model is well adapted to providing insights to these ubiquitous dependencies, which is why it has been widely adopted for forecasting and analyzing impulse responses. Despite that, only a few studies have employed the VAR model for classification. To further explore this area, we propose a simple yet effective approach based on modeling MCS with a VAR process. To demonstrate the performance of our approach, we test it on real EEG recordings to discriminate between control and alcoholic subjects. Experimental results show that the proposed VAR approach can be very effective in MCS classification; it achieves competitive results on the benchmark dataset compared to existing state-of-the-art techniques.