Nowadays, Microcontrollers are widely used in almost every electronic application, from domotic to aerospace fields. In many safety-critical applications such as Automotive Control systems, in order to meet the required high quality and safety standard, an effective performance screening has to be performed to detect under performing devices, measuring the maximum frequency at which the device can operate under challenging conditions of temperature and voltage. However, this is an extremely costly and time-consuming stage. In this work, Machine Learning techniques will be exploited in order to support the screening phase, to identify not-properly-working devices on the basis of on-board embedded ring oscillators, called Speed Monitors, that measure several physical parameters of the devices.