Identifying shared and distinct profiles, whether of symptoms, personality traits, or cognitive abilities, is essential for understanding psychological phenomena, yet doing so requires navigating interrelated theoretical, conceptual, and technical complexities. First, most psychological phenomena typically exist along continuous spectra rather than discrete categories, complicating the characterisation of inherently heterogeneous constructs. Second, studying individual differences requires balancing population-level generalisation with person-level interpretability, which is critical for applied science and clinical practice. Third, psychological data often contain missing values, non-linear relationships, and high-dimensional patterns that violate standard statistical assumptions. Commonly used approaches for profile detection often fall short of these demands, producing oversimplified or difficult-to-interpret results that are hard to map back to the individual. In contrast, self-organising maps (SOMs) offer a powerful, visual, and intuitive solution that addresses these challenges. Despite growing interest, SOMs remain underutilised in psychology, likely due to a lack of accessible tutorials and clear guidelines tailored to the field. This tutorial introduces SOMs to psychologists without technical backgrounds, emphasising practical implementation. We provide evidence-based recommendations for key methodological decisions, accompanied by annotated R code. Using a dataset of depressive symptoms as an illustrative case, we present a complete workflow covering data preprocessing, map initialisation, parameter optimisation, and interpretation. We further discuss advanced SOM extensions relevant to psychological research and highlight key limitations of the method. By making SOMs accessible, this tutorial supports nuanced, person-centred analyses that preserve the complexity of psychological phenomena.