This study suggests a multivariate-structural approach combining factor analysis and cluster analysis that could be used to examine neighborhood effects on an individual's health. Data were from the Taiwan Social Change Survey conducted in 1990, 1995, and 2000. In total, 5,784 women and men aged over 20 years living in 428 neighborhoods were interviewed. Participants' addresses were geocoded with census data for measuring neighborhood-level characteristics. The factor analysis was applied to identify neighborhood dimensions, which were used as entities in the cluster analysis to generate a neighborhood typology. The factor analysis generated three neighborhood dimensions: neighborhood education, age structure, and neighborhood family structure and employment. The cluster analysis generated six types of neighborhoods with combinations of the three neighborhood dimensions. Multilevel binomial regression models were used to assess the effects of neighborhoods on an individual's health. The results showed that the biggest health differences were between two neighborhood types: (1) the highest concentration of inhabitants younger than 15 years, a moderate education level, and a moderate level of single-parent families and (2) the highest educational level, a median level of single-parent families, and a median level of elderly concentrations. Individuals living in the first type had significantly higher chances of having functional limitations and poor self-rated health than the individuals in the second neighborhood type. Our study suggests that the multivariate-structural approach improves neighborhood measurements by addressing neighborhood diversity and examining how an individual's health varies in different neighborhood contexts.