In this paper we describe the K-Space participation in TRECVid 2006. K-Space participated in two tasks, high-level feature extraction and search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission made use of support vector machines (SVMs) created with low-level MPEG-7 visual features, fused with specific concept detectors. Search submissions were both manual and automatic and made use of both low- and high-level features. In the high-level feature extraction submission, four of our six runs achieved performance above the TRECVid median, whilst our search submission performed around the median. The K-Space team consisted of eight partner institutions from the EU-funded K-Space Network, and our submissions made use of tools and techniques from each partner. As such this paper will provide overviews of each partner’s contributions and provide appropriate references for specific descriptions of individual components.