OBJECTIVE: Individuals with chronic medical illness may have multiple health problems, and are often costly and difficult to manage. This study identifies those with highly prevalent chronic conditions and assesses related health service use and costs in the population of Emilia Romagna (RER), a large northern Italian region. METHODS: Pharmaceutical, hospital, and demographic data from 2000 and 2001 have been assembled for the entire population of RER (4 million). Pharmaceutical and hospital tariffs were a proxy for costs. Pharmacy and hospital records were used to identify individual morbidity. Data included demographic and geographic information, encounter dates, diagnosis/procedure codes, pharmaceutical information, and health care costs. Individuals were classified as having a chronic condition in 2000, and we examined pharmacy and hospital use/costs in 2001. Descriptive analyses compared mean and total costs, as well as proportions within the population. RESULTS: We identified five highly prevalent chronic conditions: cardiovascular diseases (N=824,190, 20.8% of the population); rheumatologic conditions (N=172,402, 4.4%); gastric acid disorders (N=142,191, 3.6%); chronic respiratory illnesses (N=154,601, 3.9%); and psychiatric disorders (94,140, 2.4%). 27.3% of the population had one or more of these five chronic conditions, and these individuals accounted for 72.8% of the pharmacy costs and 58.9% of the hospital costs in the following year. For persons with these five conditions, we also described use and costs by age and gender, residence, and income. CONCLUSIONS: Interestingly, one-fourth of the population with selected chronic conditions accounted for large proportions of cost and use of health services. The ability to identify those with chronic conditions would help planners and governmental agencies to address health care needs, increase quality of care, avoid unnecessary hospitalizations, and save costs. These types of data can be used to estimate health care financing and risk adjustment models, and profile specific clinical, demographic or geographic sub-groups.