This study demonstrates an application of cluster analysis to constant ambient air monitoring data of four pollutants in the Kanto region: NOx, photochemical oxidant (Ox), suspended particulate matter, and non-methane hydrocarbons. Constant ambient air monitoring can provide important information about the surrounding atmospheric pollution. However, at the same time, ambient air monitoring can place a significant financial burden on some autonomous communities. Thus, it has been necessary to reduce both the number of monitoring stations and the number of chemicals monitored. To achieve this, it is necessary to identify those monitoring stations and pollutants that are least significant, while minimizing the loss of data quality and mitigating the effects on the determination of any spatial and temporal trends of the pollutants. Through employing cluster analysis, it was established that the ambient monitoring stations in the Kanto region could be clustered topologically for NOx and Ox into eight groups. From the results of this analysis, it was possible to identify the similarities in site characteristics and pollutant behaviors.