Pornographic image detection is an important and challenging problem. Detection of pornography on the Internet is even more challenging because of the scale (billions of images) and diversity (small to very large images, graphic, grey scale images, etc.) of image content. The performance requirements (precision, recall, and speed) are also very stringent. Because of this, no single technique provides the required performance. In this paper, we describe a framework for detecting images with pornographic content. The framework combines various techniques based on object-level and pixel-level analysis of image content. To enable high-precision, we detect body parts (including faces) in images. For high-recall, low-level techniques like color and texture features are used. For adaptation to new datasets, we also support learning of appropriate color models from weakly-labeled datasets. In addition to image-based analysis, both text-based and site-level analysis are performed. Unlike many adult detection techniques, we explicitly leverage techniques like texture analysis and face detection for non-adult content identification. The multiple cues are combined in a systematic manner using ROC analysis and boosting. Evaluations on real world web data indicate that the system has the best performance among the systems compared.