Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting culti- vation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sens- ing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements, factors that influ- ence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. To validate the results, Along Track Scanning Radiometer (ATSR), the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55 63% of agreement with ATSR dataset. Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting culti- vation and spread of fires from jhum fields. mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensation and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements, factors that influ- ence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. To validate the re sults, Along Track Scanning Radiometer (ATSR), the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55 63% of agreement with ATSR datasets.