The objectives of this study were to develop metrics using empirical data for the identification of the most likely outbreak scenarios of classical swine fever (CSF) in Indiana and to describe the characteristics of the outbreaks. Three types of CSF outbreak scenarios were considered: single, multiple, and outbreak due to delay in detection. The data sources included Indiana premise identification database, feral hog population, and US census data. The attributes included in the metrics were distribution of swine premises and operation types, import frequencies (domestic and international), import origins, number of imported pigs, proximity to feral hogs, and immigrant population. Different weights were assigned to each of the attributes based on their importance. The metrics were used to identify the top 10 Indiana’s counties that were most likely to initiate a CSF outbreak; premises with high risk of outbreak were identified within the top 10 counties. Each of the identified swine premises represented a single outbreak scenario. The swine import data of the identified premises were used for identifying the multiple outbreak scenarios while the export data for identifying the outbreak due to delay in detection scenarios. These identified outbreak scenarios were simulated to derive the outbreak-related measures. In 2012, there were 8589 swine premises in Indiana. A total of 3,145 import shipments from 27 US states and 3 Canadian provinces were received. Similarly, 3,154 export shipments of live pigs were sent to 41 US states. Nineteen single and 15 multiple outbreak scenarios were identified, while no outbreak due to delay in detection was identified. The median number of premises in the multiple outbreak scenarios was 17 (range: 4–32). The estimated median epidemic durations (days) for single and multiple CSF outbreak scenarios in Indiana were 57 and 121, respectively. The identified most likely CSF outbreak scenarios can be used to estimate epidemic duration and magnitude of an outbreak and provide guidance for developing a risk-based surveillance for the CSF in Indiana.