This research demonstrates the usefulness of self-organizing maps (SOM) as an intuitive visual rendering of a globalization phenomenon. We propose a systematic neural-network-based segmentation scheme for identifying and subsequently profiling transnational segments based on consumers’ desired benefits. In the study, SOMs are used in grouping survey respondents from 16 countries in the Asia-Pacific region, Europe, South America, and North America on the basis of their expressed preference toward certain car features such as styling, sportiness, fuel economy, and safety in accidents. These car features had been shown to form four major groupings: symbolic, utilitarian, sensory, and economic. The SOM-based clustering of the data yielded these same groupings of car features, but the economic and utilitarian clusters have been further subdivided into more specific benefits clusters. These benefits clusters have been used to identify a mixture of cultural and geographic factors that would segment the world market in such a way that countries within a market segment are homogeneous in terms of distribution of benefits sought. These market segments are subsequently analyzed for their socio-demographic profile. The paper concludes that SOM is not only an effective clustering method, it also provides an insightful visual depiction of the interrelationships of the clusters by positioning them in such a way that clusters that are spatially near each other resemble each other more.