Modeling the complex human taste is an important focus in wine industries. The main purpose of this study was to predict wine quality based on physicochemical data. This study was also conducted to identify outlier or anomaly in sample wine set in order to detect adulteration of wine. In this project, two large separate datasets are used, which contains 1, 599 instances for red wine and 4, 989 instances for white wine with 11 attributes of physicochemical data such as alcohol, PH and sulfates. Two classification algorithms, Decision tree and Naïve Bayes are applied on the dataset and the performance of these two algorithms is compared. Results showed that Decision tree (ID3) outperformed Naïve Bayesian techniques particularly in red wine, which is the most common type. The study also showed that two attributes, alcohol and volatile-acidity contribute highly to wine quality. White wine is also more sensitive to changes in physicochemistry as opposed to red wine, hence higher level of handling care is necessary. This research concludes that classification approach will give rooms for corrective measure to be taken in effort to increase the quality of wine during production.