This paper introduces a method for polychotomous classification by estimating class probabilities for each pair of classes and then coupling these estimates. The approach is inspired by the Bradley–Terry model for paired comparisons. The method involves estimating pairwise probabilities and then combining them to obtain joint probability estimates for all classes. This approach is shown to perform well in both real and simulated data sets, often outperforming traditional methods like linear discriminant analysis (LDA) and the support vector machine (SVM). The paper discusses the theoretical basis of the method, including the properties of the coupling solution and its relationship to the "max-wins" rule. It also explores the benefits of pairwise threshold optimization, which allows for more flexible and accurate classification. The method is applied to various classifiers, including linear discriminants, nearest neighbors, and support vector machines, demonstrating its effectiveness in improving classification accuracy. The paper presents examples where the coupling method outperforms other approaches, particularly in cases where class probabilities are not well-separated. It also addresses the issue of class imbalance and shows how the method can be adapted to handle such scenarios. The method is shown to be robust and efficient, with computational benefits over traditional K-class methods. The paper concludes with a discussion of the broader implications of the method, including its potential applications in machine learning and statistical modeling. It also highlights the importance of considering the structure of the data and the nature of the classification problem when choosing a method. Overall, the paper provides a comprehensive overview of the coupling method and its effectiveness in polychotomous classification.This paper introduces a method for polychotomous classification by estimating class probabilities for each pair of classes and then coupling these estimates. The approach is inspired by the Bradley–Terry model for paired comparisons. The method involves estimating pairwise probabilities and then combining them to obtain joint probability estimates for all classes. This approach is shown to perform well in both real and simulated data sets, often outperforming traditional methods like linear discriminant analysis (LDA) and the support vector machine (SVM). The paper discusses the theoretical basis of the method, including the properties of the coupling solution and its relationship to the "max-wins" rule. It also explores the benefits of pairwise threshold optimization, which allows for more flexible and accurate classification. The method is applied to various classifiers, including linear discriminants, nearest neighbors, and support vector machines, demonstrating its effectiveness in improving classification accuracy. The paper presents examples where the coupling method outperforms other approaches, particularly in cases where class probabilities are not well-separated. It also addresses the issue of class imbalance and shows how the method can be adapted to handle such scenarios. The method is shown to be robust and efficient, with computational benefits over traditional K-class methods. The paper concludes with a discussion of the broader implications of the method, including its potential applications in machine learning and statistical modeling. It also highlights the importance of considering the structure of the data and the nature of the classification problem when choosing a method. Overall, the paper provides a comprehensive overview of the coupling method and its effectiveness in polychotomous classification.