Self-organizing maps (SOMs) are unsupervised neural networks. SOMs use a map structure of neurons to model training data. Data science applications usually require neurons to be labeled. Three supervised neuron labeling techniques are commonly used, namely example-centric neuron labeling (ECNL), example-centric cluster labeling (ECCL), and weight-centric neuron labeling (WCNL). The ECNL algorithm produces high-quality labels but leaves some neurons unlabeled. Label finalization techniques complete the labeling of ECNL, ensuring high-quality labels that characterize the entire map. Recent work proposes a label finalization algorithm called example-centric neuron labeling with weight-centric finalization (ECNL-WCF) and demonstrates its feasibility. This article extends this previous work by providing a comprehensive analysis of ECNL-WCF in relation to existing supervised neuron labeling methods. An empirical investigation compared the performance of ECNL-WCF to ECNL, ECCL, and WCNL on data classification tasks. For each data set a 30-fold cross-validation was performed. Training set and test set classification errors and the percentages of unlabeled neurons were measured, and performance differences were confirmed using statistical hypothesis testing. Analysis showed that ECNL-WCF classified data as accurately as ECNL, and more accurately than both ECCL and WCNL. Additionally, ECNL-WCF fully labeled maps, which both ECNL and ECCL did not achieve. This study confirms that ECNL-WCF produces accurate labels, while fully labeling SOMs. These characteristics are attractive for exploratory data analysis, where a complete labeling makes maps more interpretable, but label accuracy is also important.