During February and March 2008 New Zealand carried out a major research voyage into the Ross Sea region in support of the International Polar Year Census of Antarctic Marine Life (IPY-CAML). The 50 day voyage on the research vessel Tangaroa involved an extensive survey of marine organisms from viruses to pelagic and demersal fish and cephalopods from the surface down to depths of 3500 m, and from the continental shelf and slope of the Ross Sea to unexplored seamounts and abyssal plains immediately to the north. Pelagic and benthic sampling gear, including plankton nets, mid-water and demersal trawls, seabed cameras, sleds, and corers were deployed in each habitat to obtain samples for a broad range of research programmes led by scientists from several New Zealand research institutes and universities with collaborating scientists from the USA and Italy. Despite some of the worst ice conditions for 30 years, a total of 282 gear deployments were made at 39 sites covering a wide range of habitats. Almost 120 fish and cephalopod species were collected and nearly 4,000 benthic invertebrate sample lots were brought back for identification and further study. A total of 55 hours of seabed video and 12,500 still images were also taken using a deep towed imaging system (DTIS). The results of the survey will be directly relevant to many aspects of the work of CCAMLR and its Working Groups. An important aspect of the survey was to collect data on key species or species groups such as mesopelagic fish that will provide quantitative inputs to the Ross Sea ecosystem model. Physical and biological data collected during the survey will also contribute to work being carried out on the biodiversity and bioregionalisation in the Southern Ocean. The benthic sampling using DTIS, sleds, and trawls has already improved our understanding of the distribution and abundance of benthic invertebrates (e.g., corals, sponges) found in vulnerable marine ecosystems in the Ross Sea region, and when combined with physical data should improve our ability to predict other areas where these species are likely to occur.