In medical diagnosis blood test is very essential. For this purpose identifying the whiternblood cell type and recognizing their number are important and useful measure, which indicatesrnthe health status of the body. For the analysis of blood cell, laboratory technicians use manualrnmicroscopic evaluation which is extremely time-consuming and tedious to segment and classifyrnwhite blood cells and on the other side, the instruments which are being utilized by specialists forrnsegmentation and classification of blood cells are not economical and affordable for every doctorrnor hospital. To overcome this, various computational techniques have been developed forrnsegmentation and classification in recent years with improvements in outcomes. rnIn this respect, Artificial Neural Network (ANN) provides the ability and potentials to makernclassification. The aim of this research work is to design and implement for the classification ofrnwhite blood cell types from microscopic images of blood samples. Therefore, this research focusedrnon the tasks including the segmentation process, extract suitable features, design the classifier andrnclassify them into five types using the designed ANN model. rnThe system was experimentally analyzed with microscopic images for the classification of thernwhite blood cell types. To acquire region of interest all of microscopic images were segmented.rnSubsequently, various feature vectors were extracted from the segmented image. After thernextraction of feature vectors the classification of each microscopic image for a particular categoryrnat the next step was performed using the designed ANN model.rnThe extracted features were used as an input to the neural network. Three feature sets were usedrnto evaluate and compare the performance of the classifier. Accordingly, the segmentation resultsrnshow that k-means clustering outperforms Otsu thresholding with an average segmentationrnaccuracy of 91.6% and 88.2% respectively. The designed classifier model also yields arnclassification accuracy of 93.8% to 96.5% based on extracted features from segmented images. Itrnis understood that this research provides the possibility of increasing the speed to find the resultsrnof medical analysis by using ANN especially as the number of blood samples increase.