Brazilian Savanna, or just Cerrado, is considered one of the 25 hotspots for biodiversity conservation priority in the world. Cerrado occurs on the central part of Brazil and has three major natural formations: Grasslands, Savannas and Forests. However, the challenge on mapping Cerrado relies on the division of these major formations into specific physiognomies. Distinguishing each of these physiognomies is an important task to better evaluate smaller ecosystems, access carbon storage with greater precision and improve the exactitude of greenhouse gases emissions. Thus, the aim of this work is to evaluate the potential of very high spatial resolution imagery in order to improve the classification of 8 Cerrado physiognomies: Rocky Grasslands, Open Grasslands, Shrub Grasslands, Shrub Savanna, Typical Savanna, Dense Savanna, Flooded Plains with Palmtrees and Evergreen Forest. A WorldView-2 image was used for a protect area with over 30 thousand hectares of preserved Cerrado vegetation. Features such as surface reflectance, vegetation indices, tasseled cap transformation and spectral linear mixture models were used on the automatic classification. Random Forests algorithm was used with a 10-fold cross-validation. The Global Accuracy was of 67.7%. Values above 70% of User's Accuracy were obtained for classes such as Rocky Grasslands, Open Grasslands, Typical Savanna and Evergreen Forest. On the other hand, Flooded Plains with Palmtrees were omitted from the classification. Omission errors were also noticed for the classes of Shrub Savanna and Dense Savanna; they were sometimes misclassified as Typical Savanna which has a similar vegetation structure and tree cover percentage. The use of very high resolution images provided advantages on distinguishing Cerrado physiognomies on an automatic classification procedure. The detection of some classes was very precise and, despite the obtained misclassifications, it is an advance to distinguish some physiognomies that lower spatial resolution sensors are, hardly never, capable of distinguishing.