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Visually evoked potential (VEP) is an electrical signal generated by the brain (Occipital Cortex) in response to a visual stimuli. These VEP are recorded non-invasively by placing the surface electrodes at the scalp, and observed as a reading on an electroencephalogram (EEG). VEP signal has been widely used for the diagnostics of vision impairments in patients. The main parameters that were considered for the diagnostics of these diseases are the amplitude and the latency values. This field of study is gaining interest from researches all over the world. In this paper, time domain based features of the VEP is studied in an effort to discriminate normal subjects from those having vision impairments. Three different classifiers, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and the k Nearest Neighbor (kNN) are used for the investigation. The proposed method shows promising results in the investigation of vision impairments with accuracy ranging from 69.44% to 100%.