Leukemia is a cancer of the blood and bone marrow, the spongy tissue confidential the bones where blood cells are made. Acute myeloid leukemia (AML) is one of the most common types of leukemia among adults. The signs and symptoms of leukemia are non-specific in nature and also they are comparable to the symptoms of other mutual disorders. Manual microscopic inspection of stained blood smear or bone marrow aspirate is the only way to an effective diagnosis of leukemia. But this method is time-consuming and less accurate. In this paper, a technique for automatic detection and classification of AML in blood smear is presented. K-means algorithm is used for segmentation. KNN, NN and SVM are used for classification. GLCM is used for optimising the spectral features. The local binary pattern is used for texture description. Blood microscope images were tested and the performance of the classifier was analyzed.