Centralized statistical monitoring (CSM) detects clinical trial centers in which the distribution of a variable is atypical compared to its distribution in other centers.Most proposed CSMmethods concern quantitative variables. Here we propose a new hierarchical Bayesian beta-binomial (HBBB) method for categorical variables and report the results of a simulation study assessing the performance of the method and of an application study using a real database to assess its usefulness. In the simulation study, sensitivity exceeded 90% when the sample size in the atypical center (Na)was≥20 and the difference in the proportion of events between the atypical center and the other centers (δ) was ≥0.4; when Na was ≥40 and δ ≥0.3; and when Na was ≥150 and δ ≥0.2. Specificity exceeded 90% when Na was ≥150 in all scenarios, and remained between 75% and 90% when Na was lower. In the application study, the method detected two centers in which Na was 50 and 200, and δ was 0.12 and 0.04, respectively. The performance of the HBBB method was similar to that proposed by competing approach. The modeling is easy and specificity is good in many scenarios with a limited sample size.