Cruciferous weeds are competitive broad-leaved species that cause losses in winter crops. In the present study, research on remote sensing was conducted on seven naturally infested fields located in Córdoba and Seville, southern Spain. Multi-spectral aerial images (four bands, including blue (B), green (G), red (R) and near-infrared bands) taken in April 2007 were used to evaluate the feasibility of mapping cruciferous patches ( Diplotaxis spp. and Sinapis spp.) in winter crops (wheat, broad bean and pea) and compare the accuracy of different supervised classification methods (vegetation indices, maximum likelihood and spectral angle mapper). The best classification method was selected to develop site-specific cruciferous treatment maps. Cruciferous patches were efficiently discriminated with red/blue (R/B) and blue/green (B/G) vegetation indices and the maximum likelihood classifier. At all of the locations, the accuracy of the results obtained from the spectral angler mapper was relatively low. The cruciferous weed-classified imagery of each location were created according to the method that provided the best discrimination results and were used to obtain site-specific treatment maps for in-season post-emergence control measures or herbicide applications for subsequent years. By applying the site-specific treatment maps, herbicide savings from 71.7 to 95.4% for the no-treatment areas and from 4.3 to 12% for the low-dose herbicide were obtained.