This study was designed to identify the risk factors of unplanned readmission in a university hospital. The six-month discharge information from January to June, 2000 in a tertiary university hospital was used as a source of data through the medical record and hospital information system. To increase the effect of comparison. the data were collected by sampling 192 couples (384 patients) of unplanned readmission group through the matching by its disease groups, sex, and age. The accuracy of prediction for unplanned readmission was analyzed by constructing the predicted model of unplanned readmission through the logistic regression. The study results are as follows. The conditional logistic regression analysis was performed with nine variables at the significance level 0.05 through univariate analysis including residence, days after discharge, initial admission route, previous admission, transfer to special care unite, hospital stay days, medical care expenses, special cares, and laboratory and imaging services. As a result, the closer the patients live in Seoul and Gyeong-in area (Odds ratio=2.529, p=0.003), the shorter the days after discharge was (Odds ratio=0.600, p=0.000), and the more frequent admission rate was (Odds ratio=2.317, p=0.004), the more unplanned readmission was resulted. Also, the accuracy of prediction for data classification of this regression model showed $70.3\%$(032+83/306).