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There is a large discrepancy between studies on how the Indian Ocean Dipole (IOD) is defined. Indeed, the definition of historical events varies substantially, resulting in conflicting classifications, hampering hydroclimatic impact assessment of this phenomenon. Therefore, this paper aims to compare the various methods that have previously been adopted to define IOD events and demonstrate an alternative method that specifically captures different flavours of IOD. It is shown that the choice of sea surface temperature (SST) data set and climatological baseline has significant impact on the resulting classification of IOD events. Furthermore, not all events display the traditional dipole signature pattern of warming/cooling occurring at opposite poles. Rather, some events could be considered pseudo dipoles, whereby the SST anomalies in the two poles are not of opposite sign. Importantly, the different types of IOD (i.e., pseudo and true) are associated with varying impacts on rainfall for surrounding regions. To overcome these issues, this study treats Indian Ocean SST anomalies as a continuum of a finite maximum number of statistically distinguishable patterns using a neural network-based cluster analysis. Ten individual SST patterns were identified with distinct rainfall teleconnections. In particular, four strong IOD patterns (two positive and two negative) were identified, alongside three pseudo dipoles representing anomalously warm conditions across the entire Indian Ocean Basin, with a SST gradient towards the east (i.e., a pseudo negative dipole) or west (i.e., a pseudo positive dipole). The occurrence of each of the 10 self-organizing mapping (SOM) patterns is shown to be variable over the past 130years, with some types more common during certain phases of the El Nino/Southern Oscillation or during the first (or second) half of the 20th century. The implications of these findings are particularly significant for attribution studies, development of proxy climate reconstructions, climate model evaluation and future climate change assessment.