In this study; A low cost electronic nose circuit consisting of different sensors was used for classification of six different brewed tea. The odour of thebrewed teas are transformed into electric signal by an electronic nose circuit with eight sensors inside. These signals are classified using normalization, feature extraction, size reduction and classification algorithms. The principal component analysis method is used to reduce the size of the attribute matrix. For the classification, linear discriminant analysis, support vector machines and decision trees algorithms are used. 10-fold cross-validation method was used to determine the classification success. As a result of this study, the highest classification success was obtained as 70.9 % with linear discriminant analysis algorithm.