This thesis work tried to realize a Machine Learning algorithm capable of autonomously identify and detect the application status monitored by the APM organizational unit. To this end, two different approaches were adopted: \textbf{Supervised} and \textbf{Unsupervised Learning}. Both can be used in real use case scenarios, depending on the type of monitoring required. A mathematical and theoretical explanation of the various Machine Learning types was given, highlighting the necessary characteristics to know in order to understand the work carried out. The Supervised Learning consisted in the realization of a classifier (created in two different libraries) able to distinguish the application state after being trained on a manually exported dataset.