Text line segmentation is an inherent part of document recognition system and important preprocessing step for word and character segmentation. Presence of touching or overlapping text lines, short-lines, curvilinear or skewed lines and small or variant gaps between the text lines make the segmentation challenging. These variations cause errors in recognition phase. This paper describes the topdown approach of handwritten text line segmentation. The proposed method begins with core detection. To segment the overlapping components, run-length is used for obtaining the structural knowledge which classifies the components into upper and lower text lines. To segment the short lines and skewed lines, distance metrics and connected component are used recursively. The system was evaluated using 200 images from the IAM database and 100 documents collected from different writers. From the experiments conducted, it was observed that the system has 91.92% accuracy and imbibes in its reliability. General TermsDocument Image Processing, Image Segmentation.