U.S. EPA recently released its new mobile source emission model, MOVES2010, which reflects several significant updates from its predecessor, MOBILE6. MOVES is a new modelling platform built to support multiple scale analysis, from detailed “project level” assessments to emission inventories at the regional or national level, for greenhouse gases, so-called “criteria” air pollutants, and air toxics. To support multiple scale analysis, MOVES has adopted a “modal” emission approach, which provides more flexibility in predicting emissions for different driving patterns, and allows assessment of emission impacts due to changes in vehicle acceleration as well as vehicle speed. Using a modal approach also enables a much broader assessment of vehicle emissions from multiple data sources, including inspection/maintenance programs, remote sensing data, portable emission measurement systems (PEMS), and traditional laboratory data. The updated emission estimates from MOVES2010 show significant increases in NOx and PM emissions relative to MOBILE6, which have been verified against independent data sources. MOVES2010 was developed to allow customization to local areas, so that U.S. state and local governmental agencies can satisfy legislative mandates for air quality and transportation planning. These customization features give MOVES broad flexibility for international application as well. This paper discusses different “tiers” for international customization of MOVES. A first level would be to input custom vehicle fleet and activity data such as vehicle age distribution, vehicle distance travelled, and vehicle population; this level of customization could proceed quickly if such local data were already available. A second level of customization would focus on developing vehicle emission rates reflecting the emission standards applicable to the country being modelled. A final level of customization would be to implement more fundamental changes such as adding vehicle types, road types, or driving patterns. This paper will provide details on this tiered approach, with some consideration for the trade-off between increased customization and data collection burden.