Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular diseases. Due to the complex nature of retinal vessel network, the manual segmentation of vessels is a tedious task which also requires high training and skills. This study presents a new method for blood vessel segmentation in colour retinal images using supervised approach. Initially, a set of core features including Gabor filter responses, Frangi's vesselness measure (1D), local binary pattern feature (1D), Hu moment invariants (7D) and grey-level co-occurrence matrix features (3D) are considered. The neural network is trained with the different subsets of core features and it is found that the model with 13D features excluding the Hu moment invariants results in better performance. This model is used for evaluation. The proposed supervised segmentation approach is tested on publicly available structured analysis of the retina, digital retinal images for vessel extraction and CHASE_DB1 databases which contain manually labelled images. The performance of the proposed algorithm is evaluated on the basis of accuracy, sensitivity, specificity and area under the curve. The proposed technique achieves high mean accuracy and sensitivity while it is compared with the several previously proposed algorithms.