In recent years, efforts have increased to develop quantitative, computer-directed methods for segmentation of multibeam (MBES) backscatter data. This study utilises MBES backscatter data acquired at Stanton Banks (UK) and subsequently processed through the QTC-Multiview software environment in a bid to evaluate the program's ability to perform unsupervised classification. Statistical comparison with ground-truth data (grab, stills and video) enabled cross validation of acoustic segmentation and biological assemblages observed at the site. 132 unspecified variables were extracted from user-specified rectangular patches of the backscatter image, reduced to three vectors by PCA, then clustered and classified by the software. Multivariate analyses of ground-truth data were conducted on 75 stills images and 51 grab samples. Video footage coincident with the stills was divided into 30 s segments and coded by dominant substrate and species. Cross tabulation determined the interrelationship between software classifications, multivariate analysis of the biological assemblages and coded video segments. Multiview optimally identified 19 classes using the automated clustering engine. These were revised to 6 habitats a posteriori, using combined analysis of ground-truth data and Multiview data products. These habitats broadly correspond to major physiographic provinces within the region. Multivariate statistical analysis reveals low levels of assemblage similarity (< 35%) for samples occurring within Multiview classes, irrespective of the mode of acquisition. Coded video data is more spatially appropriate than the other methods of ground-truthing investigated, although it is less well suited to the extraction of truly quantitative data. Multivariate analysis indicates assemblages within physiographically distinct Multiview classes have a low degree of biological similarity, supporting the notion that abiotic proxies may be contraindicative of benthic assemblage variations. QTC-Multiview performs well as a mechanism for computer-assisted segmentation of MBES backscatter imagery into acoustic provinces; however a degree of caution is required prior to ascribing ecological significance to these classifications. (c) 2008 Elsevier Ltd. All rights reserved.