This article introduces the SPAWNN toolkit, an innovative toolkit for spatial analysis with self‐organizing neural networks, which is published as free and open‐source software (http://www.spawnn.org). It extends existing toolkits in three important ways. First, the SPAWNN toolkit distinguishes between self‐organizing neural networks and spatial context models with which the networks can be combined to incorporate spatial dependence and provides implementations for both. This distinction maintains modularity and enables a multitude of useful combinations for analyzing spatial data with self‐organizing neural networks. Second, SPAWNN interactively links different self‐organizing networks and data visualizations in an intuitive manner to facilitate explorative data analysis. Third, it implements cutting‐edge clustering algorithms for identifying clusters in the trained networks. Toolkits such as SPAWNN are particularly needed when researchers and practitioners are confronted with large amounts of complex and high‐dimensional data. The computational performance of the implemented algorithms is empirically demonstrated using high‐dimensional synthetic data sets, while the practical functionality highlighting the distinctive features of the toolkit is illustrated with a case study using socioeconomic data of the city of Philadelphia, Pennsylvania.