Through the development of artificial intelligence, some capabilities of human beings have been replicated to computers. Among the models developed, Convo lutional Neural Networks stand out considerably because they make it possible for systems to have inherent capabilities of humans, such as pattern recognition in ima ges and signals. However, conventional systems are based on deterministic models, which are unable to express the epistemic uncertainty of their predictions. The al ternative consists in the use of probabilistic models although these are considerably more difficult to develop. In order to address the problems related to the development of probabilistic networks and the choice of the network architecture, in this disserta tionthe development of an application is proposed , which allows the user to choose the desired architecture and obtain the model already trained for the given data. This application named “Graphical Interface for Probabilistic Neural Networks” gi ves the user the possibility to use the most common Convolutional Neural Networks for different data sets, being the networks adapted to the developed probabilistic model. Contrary to existing models for generic use, which are deterministic and already pre-trained on databases to be used in transfer learning, the approach fol lowed in this work creates the network layer by layer, with training performed on the provided data, originating a specific model for the data in question.