Wireless capsule endoscopy (WCE) is a modern and efficient technology to diagnose complete gastrointestinal tract (GIT) for various abnormalities. Due to long recording time of WCE, it acquires a huge amount of images, which is very tedious for clinical expertise to inspect each and every frame of a complete video footage. In this paper, an automated color feature based technique of bleeding detection is proposed. In case of bleeding, color is a very important feature for an efficient information extraction. Our algorithm is based on statistical color feature analysis and we use support vector machine (SVM) to classify WCE video frames into bleeding and non-bleeding classes with a high processing speed. An experimental evaluation shows that our method has promising bleeding detection performance with sensitivity and specificity higher than existing approaches.