The recognition of geochemical patterns, including the spatial association of geochemical patterns, geochemical element associations, and geochemical anomalies related to mineralization, requires different methods to meet the corresponding requirements. In this study, a self-organizing map (SOM), an unsupervised learning technique, was employed to realize the whole processes of geochemical pattern recognition in southeastern Hubei Province, China. First, the clustering ability of SOM was adopted to reveal the spatial association of geochemical patterns; then, geochemical elemental associations were adopted according to the association between variables and clusters derived from the correlation analysis between the unified distance matrix (U-matrix) and SOM maps, and finally the quantification error (QE) of the SOM was adopted as an anomalous index to detect multivariate geochemical anomalies related to mineralization. The obtained results show that two of the clusters are spatially closely related to ore-forming geological bodies, that is Precambrian strata and Yanshanian intrusive rocks. The associations of Au–As–Ba–Cd–Sb–V and Au–Ag–Pb–Zn–Bi–Cu–Mo–Na2O–P–Sr–W are responsible for the characterization of the two clusters, and their combination was further used for multivariate geochemical anomaly detection. The extracted multivariate geochemical anomalies are closely spatially correlated with known gold polymetallic deposits. Both the receiver operating characteristic (ROC) and success-rate curves suggest a satisfactory anomaly detection results, which can provide guidance for further mineral exploration. The results of this study also suggest that SOM is an effective and interpretable tool for a better recognition of geochemical patterns related to mineralization.