The proposed procedure ideas a tree using hierarchical clustering, and then organizes the cells in a lined order by using an optimal leaf ordering algorithm that maximizes the parallel of adjacent leaves without any must for training examples or parameter regulation. Performance estimation using two data sets show the efficacy of the proposed tactic in images having uneven staining, poor contrast, and overlapping cells. The cure rate is strictly related to the stage of the virus at diagnosis time, with a very high prospect of fatality if it is left untouched. The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells.