Image databases are getting larger and diverse with the coming up of new imaging devices and advancements in technology. Content-based image classification (CBIC) is a method to classify images from large databases into different categories, on the basis of image content. An efficient image representation is an important component of a CBIC system. In this paper, we demonstrate that Self-Organizing Maps (SOM)-based clustering can be used to form an efficient representation of an image for a CBIC system. The proposed method first extracts Scale-Invariant Feature Transform (SIFT) features from images. Then it uses SOM for clustering of descriptors and forming a Bag of Features (BOF) or Vector of Locally Aggregated Descriptors (VLAD) representation of image. The performance of proposed method has been compared with systems using k-means clustering for forming VLAD or BOF representations of an image. The classification performance of proposed method is found to be better in terms of F-measure (FM) value and execution time. © Springer Science+Business Media Singapore 2017.