Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and it's origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.