This paper presents an operational approach to predicting fire growth for wildland fires in Canada. The approach addresses data assimilation to provide predictions in a timely and efficient manner. Fuels and elevation grids, forecast weather, and active fire locations are entered into a fire-growth model; then predicted fire perimeters are mapped and presented on the web. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite-based detection systems are used to detect current wildland fires (referred to as hotspots). For selected regions, fire-growth simulation environments are assembled. Fuel type data from several fire management agencies are available in grid format at a resolution of 100 m or less; in areas where such data are not available, a national fuels map based on Satellite Pour l’Observation de la Terre Vegetation sensor (SPOT VGT) land cover and forest inventory is used. Similarly, terrain data are available from a variety of sources. Current hotspots are used as ignition points while past hotspots are used to delineate area burned. Surface wind, temperature, and dew-point values (forecast by Environment Canada) are used to determine the fire weather conditions at the fire location. A case study of two large fires in Canada consisting of 54 fire simulation days is used to test these hypotheses.