Leukemia (blood cancer) begins in the bone marrow and causes the formation of a large number of abnormal cells. The most common types of leukemia known are Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia (AML), Chronic lym- phocytic leukemia (CLL) and Chronic myeloid leukemia (CML). This thesis makes an e¿ort to devise a methodology for the detection of Leukemia using image process- ing techniques, thus automating the detection process. The data set used comprises of 220 blood smear images of leukemic and non leukemic patients. The Image segmentation algorithms that have been used are k means clustering algorithm, Marker controlled watershed algorithm and HSV color based segmentation algorithm. The morphological components of normal and Leukemic lymphocytes di¿er significantly; hence various features have been extracted from the segmented lymphocyte images. The leukemia is further classified into its types and sub types by making use of the SVM classifier, which is a Machine Learning classifier.