The relationship between the incidence of bacillary dysentery and meteorological factors was investigated. Data on bacillary dysentery incidence in Shenyang from 1990 to 1996 were obtained from Liaoning Provincial Center for Disease Control and Prevention, and meteorological data such as atmospheric pressure, air temperature, precipitation, evaporation, wind speed, and the amount of solar radiation were obtained from Shenyang Meteorological Bureau. Kendall and Spearman correlations were used to analyze the relationship between bacillary dysentery and meteorological factors. The incidence of bacillary dysentery was treated as a response variable, and meteorological factors were treated as predictable variables. Software R 2.3.1 was used to execute the classification and regression trees (CART). The model improved the accuracy of the fitting results. The residual sum square error of the regression tree model was 53.9, while the residual sum square error of the multivariate linear regression model was 107.2. Among all the meteorological indexes, relative humidity, minimum temperature, and pressure one month prior were statistically influential factors in the multivariate regression tree model. CART may be a useful tool for dealing with heterogeneous data, as it can serve as a decision support tool and is notable for its simplicity and ease.