Indonesia produces the majority the world's palm oil, accounting for approximately 44%. Since 2006 Indonesia is the biggest crude palm oil (CPO) exporter in the world (Sukamto, 2008). Oil palm is a valuable sector to support Indonesian economics but it also causes environmental impacts. Sustainable oil palm development (SOPD) is a key important to improve quality of oil palm development. Remote sensing technology then can be utilized to support SOPD. This study has tried to classify growing stages of oil palms using high spatial resolution FORMOSAT-2 satellite image. FOMOSAT-2 data has 4 multispectral bands (8 m) and 1 panchromatic band (2m). Because oil palms in plantation has triangular planting pattern where a space between oil palm trees is about 9 meter, the 2 m panchromatic band can be used to recognize this planting pattern by texture calculation. Texture extraction using image matching by correlation and supervised image classification using maximum likelihood classifier have been used in this study. In detail, image classification attempted to use only multispectral data and the combination of multispectral data plus texture information. Both results then were compared. The study area was Cimulang oil palm plantation in West Java province, Indonesia. The result shows that the overall accuracy (OA) of 66.4% is achieved from the image classification that used only multipectral bands. The OA of 76.8% is achieved from the image classification that used multi spectral plus texture information. By adding texture information to multispectral bands in classification, the OA is improving 10.4%.