Different pathological and physiological activities of the brain can be analyzed by means of utilizing Electroencephalography (EEG) signals. One such important activity which can be assessed and understood with the help of electrical representation of the brain signals is alcoholism. Alcoholism is a serious concern to many in the world as it affects the vital organs of the human body like liver, brain, lungs, heart, blood, immunity levels etc. In the arena of biomedical research, classification of alcoholic subjects from EEG signals is quite a challenging task. In this paper, the alcoholic EEG signals are analyzed comprehensively for a single alcoholic patient and it is classified with many post classifiers. Initially Correlation Dimension features are extracted from the EEG signals and then it is classified with the help of Detrend Fluctuation Analysis (DFA). In order to improve the classification accuracy further, it is again classified with 6 other post classifiers such as Linear Discriminant Analysis (LDA), Kernel LDA, Firefly algorithm, Gaussian Mixture Model (GMM), Logistic Regression (LR) and Softmax Discriminant Classifier (SDC). Results report a high classification accuracy of 97.91% when GMM is employed followed by a classification accuracy of 97.33% when Logistic Regression is employed. A comparatively low classification accuracy of 89.6% is obtained when LDA was employed.