Latent class analysis (LCA) is a well-established research method in social science for explaining observed correlations or vari- ance by identifying latent classes, but it has rarely been applied in urban studies. This paper provides an empirical case of using LCA to identify the generic growth patterns of the high-tech manufacturing industry across locations in the Seoul metropolitan area (SMA). The presented model uses standardized high-tech industry growth data processed from firm registration data in SMA (2009–2014) as outcome variables, and incorporates the initial high-tech firm density in 2009 as a covariate, aiming to explore the possible link between identified growth patterns and initial high-tech firm density. The authors found that during 2009–2014 continuous growth of the high-tech industry was more likely to occur in locations with relatively low initial high-tech firm density. As firm density rose, fewer locations could have sustaining growth but increasingly relied on certain triggers to achieve further growth. The probability of falling into industrial decline also increased as high-tech firm density grew. In addition, locations with relatively low high-tech firm density were more likely to experience big fluctuations. The methodology and the findings presented in this paper are expected to contribute to industrial location choices and development studies in similar metropolitan areas. DOI: