AIS data, comprising spatiotemporal information regarding the ships at sea, can be exploited to generate insights into maritime traffic by use of various data analytic techniques. Amongst the commonly used techniques for undertaking data analysis on AIS information is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and one of its adaptations, ST-DBSCAN (Spatio-temporal DBSCAN). While undertaking DBSCAN and ST-DBSCAN clustering, points are clustered together based on their proximity measures by a distance metric. This paper examines the effect of selected distance metrics on ST-DBSCAN and multidimensional DBSCAN clustering of real AIS data and evaluates their performance using clustering performance metrics, with a focus towards the determination of individual trajectories. Based on the experiments conducted, the paper suggests the preferred distance metrics to be adopted while clustering maritime movement data using DBSCAN and ST-DBSCAN.