Blood vessel segmentation in retinal fundus images is the first step for the diagnosis and treatment of diseases such as diabetic retinopathy, glaucoma and age related macular degeneration. In this paper, several supervised classification methods with adapted features are used in order to extract blood vessels in color retinal fundus images. Furthermore, the obtained results are compared against each other in terms of computational durations and classification accuracy. Firstly, a pixel based feature extraction method is performed in which features are extracted from the enhanced images of the vessel. Afterwards, a classification stage is performed to decide whether a pixel belongs to a vessel or the retinal background using these features. K-nearest neighbors, Naïve Bayes and support vector machines are used as supervised classification mechanisms. Retinal fundus images from two publicly available database STARE and DRIVE are used for performance evaluation. Obtained performance values and computation time results are compared. As a result, it is observed that Naïve Bayes classifier is the fastest method and support vector machines method has the highest accuracy.