Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure because it was found to be correlated to the operational performance, the total ridership, consumer experience, passenger flow distribution, and network resilience. A handful number of past studies characterized and compare PTNs structure, but little is known about broader or general classification worldwide. This study examined how PTNs can be clustered into groups when considering multiple features. Centralization, accessibility, robustness, service connectivity and directness are five main considered network features used in previous studies to analyze the network structure. K-means, hierarchical clustering and principal component analysis were performed to identify the cluster of PTNs defined from those five features. To illustrate the method, we conducted a case study of 20 real-life rail-bound networks worldwide generated by the up-to-date general transit feed specification (GTFS) data. As a result, we were able to identify four main meaningful clusters: tram, tram-related, metro and tram and mixed modes. Although modes of transportation and the size of the network were not parts of features, they heavily influence the clusters. The proposed method show automatic and reproducible tools to empirically identify topological patterns of PTNs.