This paper presents five classifier to classify pap smear single cell image using quantitative features. the five classifier include the naive bayes (NB), multi-layer perceptrons (MLPs), the instance-based learning algorithm (IBL), decision tree learning algorithm (J48), and repeated incremental pruning to produce error reduction (Jrip). erik martin has used 7 categories of pap smear cell image class. three classes of which are normal cell image class categories that include: normal superficial, normal intermediate, and normal columnar, and the other four classes are categories of abnormal cell image cell image class that include: mild (light) dyplasia, moderate dysplasia, severe dysplasia and carcinoma in site. this paper presents the result of a study on the decision combination of different classifiers is proposed and evaluated using seven pap approach of combining classwise expertise of diverse classifiers is proposed and evaluated using seven papsmear cell image cllassed based on 20 quantitative features. the experimental study hows that in all(7)-class classification using the five classifiers with the weka correctly classification instances (CCI) and kappa coefficient classification performance measures, the multi-layer perceptrons classifier performs the best with the CCI of 71.43% and the kappa coefficient of 0.67. in the case of 2-class classification, the decision tree J48 performs the best with the CCI of 96.67% and the kappa coefficient of 0.93.