We present a model which is applied to frequencies in cells of an m X m contingency table (confusion matrix) of medical Pap test categories (results) compared to the final histological diagnosis--a typical pattern recognition process involving classification of objects in medicine. This model defines numerical scales, which represent morphological dissimilarities among different Pap test categories or classes. Using this model, we fit the m X m two-way discrete classification table of Pap test by maximizing the likelihood and computing the corresponding morphological scales of classes. The model predicts the probability of errors (or 'confusion') of the Pap test data numerically. It estimates the scales of the discrete categories or classes in Pap test results, and uses these scales to represent relative distances between classes. By relative distances, one can identify a frequently confused pair of classes in Pap tests. Similar application of the model to ovarian tumor diagnosis also identifies 'confused' pairs in tumor types of diagnosis. With our experience, the model we developed provides a quantitative means in assessing the appropriateness of classifications in pattern recognition. It identifies the causes of mis-classification of objects and characterizes the closeness of morphologies of different classes by numerical relative distances. These relative distances help us determining when two classes should be divided or combined and, thus, effectively identifying objects in a pattern recognition process.