Combining two or more classifiers into an ensemble and fusing the individual classifier decisions to a consensus decision can improve the accuracy for a classification problem. The classification improvement of the fusion result depends on numerous factors, such as the data set, the combination scenario, the decision fusion algorithm, as well as the prediction accuracies and diversity of the multiple classifiers to be combined. Due to these factors, the best decision fusion algorithm for a given decision fusion problem cannot be generally determined in advance. In order to support the user in combining classifiers and to achieve the best possible fusion result, we propose the PUSION (Python Universal fuSION) framework, a novel generic and automated framework for decision fusion of classifiers. The framework includes 14 decision fusion algorithms and covers a total of eight different combination scenarios for both multi-class and multi-label classification problems. The introduced concept of AutoFusion detects the combination scenario for a given use case, automatically selects the applicable decision fusion algorithms and returns the decision fusion algorithm that leads to the best fusion result. The framework is evaluated with two real-world case studies in the field of fault diagnosis. In both case studies, the consensus decision of multiple classifiers and heterogeneous fault diagnosis methods significantly increased the overall classification accuracy. Our evaluation results show that our framework is of practical relevance and reliably finds the best performing decision fusion algorithm for a given combination task.