Recently, there has been a dramatic increase of the amount of audio, video, and images created and shared on the internet by users around the world. Much of this content is publicly available and free of cost. When viewed through the lens of pattern classification, this content can be seen as a virtually unlimited supply of training data for various statistical modeling and labeling tasks such as speech recognition and computer vision. In order to effectively exploit this data resource, significant research challenges must be addressed. In this paper, we present three significant challenges that must be solved to harness the potential of this “data deluge”. We then describe recent work in spoken language processing and image processing that has begun to address these challenges in order to tackle large-scale classification tasks. By bringing together the work of these two communities, we hope to stimulate the cross-pollination of ideas and methods among different signal processing communities.