As mass‐spectrometry‐based proteomics has matured during the past decade, a growing emphasis has been placed on quality control. For this purpose, multiple computational quality control tools have been introduced. These tools generate a set of metrics that can be used to assess the quality of a mass spectrometry experiment. Here we review which types of quality control metrics can be generated, and how they can be used to monitor both intra‐ and inter‐experiment performances. We discuss the principal computational tools for quality control and list their main characteristics and applicability. As most of these tools have specific use cases, it is not straightforward to compare their performances. For this survey, we used different sets of quality control metrics derived from information at various stages in a mass spectrometry process and evaluated their effectiveness at capturing qualitative information about an experiment using a supervised learning approach. Furthermore, we discuss currently available algorithmic solutions that enable the usage of these quality control metrics for decision‐making.