Diagnosis of ophthalmologic and cardiovascular systems most often rely on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in the retinal fundus images can aid in the early screening or detection of many ophthalmological diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation using improved SOM (iSOM) and ANN classifier is presented. Morphological operations are carried out to enhance the image. Clustering of pixels is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein a new node is introduced and new learning methodology is adopted using constrained weight updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and non –vessel. Segmentation is tested in public image sets, High Resolution Fundus (HRF) images and DRIONS-DB databases. The results are evaluated and achieve an appreciable level of accuracy (~97%) as compared to other similar methods of classification.