Lane detection is a problem that has been extensively studied by the research community in the past two decades. However limited literature can be found on techniques to distinguish the various types of lane markings - such as solid, dashed, single, double, zigzag etc. In this paper, we present a modular approach to detect and distinguish a wide range of lane markings. The fundamental processing module for detecting basic lane markings (BLM) is first proposed, after which we show how this can be deployed for distinguishing the various lane marking types. The underlying principle is that any lane marking can be broken down into one or more BLMs. A modular architecture is presented to detect and distinguish the various lane markings using the proposed modules. The techniques are evaluated on the road marking dataset in [8] and is shown to yield a high detection accuracy.