In this study, a chemometric approach was established for classifying honey samples based on sugar content, phenolics, antioxidant properties, and color values. Fifty-two different honey samples were obtained from different regions of Turkey, including cotton, chestnut, sunflower, honeydew, citrus, and canola. Sugar content, phenolic profile, total phenolic content, CIE-color values (L*, a*, b*), oxygen radical absorbance capacity, and Trolox equivalent absorbance capacity of honeys were determined. Different display and pattern recognition techniques were used to classify those honey samples. Fifteen variables were reduced to six principal components by principal component analysis, and 78.31% of total variance could be explained. Three main and six sub-clusters were identified in cluster analysis. The model and cross-validation rates for classification by the discriminant analysis (DA) were found as 87.8% and 49.0%, respectively. Artificial neural networks showed better performance than DA, probably due to the model optimization using hidden layers with varying neurons and input variables.