In order to recognize multi-class cervical cells automatically, a hierarchical method with PCA (principal component analysis) feature transformation was proposed and this cell recognition could provide the evidence for cervical cancer diagnosis. The cervical cell recognition was treated as a 4-class classification problem. There were two levels in this hierarchical method. First, one-versus-one strategy was used to train 6 SVM (support vector machine) classifiers to do a 3-class classification. Second, abnormal cells in one type of 3 categories were classified by a 2-class SVM. To optimize the feature combination and reduce the running time, a feature transformation method named PCA was adopted to transform the original feature vector into lowdimension feature space. The experiments show that the proposed hierarchical PCA recognition method is faster than the common hierarchical method at a ratio of 21.31%, and can distinguish 4 cervical cell categories better than 6 other traditional methods and achieve above 90% accuracy.