BACKGROUND: There is controversy as to whether use of statistical clustering methods to identify common disease patterns in schizophrenia identifies patterns generalizable across countries. OBJECTIVE: The goal of this study was to compare disease states identified in a published study (Mohr/Lenert, 2004) considering US patients to disease states in a European cohort (EuroSC) considering English, French, and German patients. METHODS: Using methods paralleling those in Mohr/Lenert, we conducted a principal component analysis (PCA) on Positive and Negative Syndrome Scale items in the EuroSC data set (n=1,208), followed by k-means cluster analyses and a search for an optimal k. The optimal model structure was compared to Mohr/Lenert by assigning discrete severity levels to each cluster in each factor based on the cluster center. A harmonized model was created and patients were assigned to health states using both approaches; agreement rates in state assignment were then calculated. RESULTS: Five factors accounting for 56% of total variance were obtained from PCA. These factors corresponded to positive symptoms (Factor 1), negative symptoms (Factor 2), cognitive impairment (Factor 3), hostility/aggression (Factor 4), and mood disorder (Factor 5) (as in Mohr/Lenert). The optimal number of cluster states was six. The kappa statistic (95% confidence interval) for agreement in state assignment was 0.686 (0.670–0.703). CONCLUSION: The patterns of schizophrenia effects identified using clustering in two different data sets were reasonably similar. Results suggest the Mohr/Lenert health state model is potentially generalizable to other populations.