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 different types of quality control metrics can be generated, and how they can be used to monitor both intra- and inter-experiment performance. 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 performance. 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. This is the peer reviewed version of the following article: "Bittremieux, W., Valkenborg, D., Martens, L. & Laukens, K. Computational quality control tools for mass spectrometry proteomics. PROTEOMICS 17, 1600159 (2017)", which has been published in final form at https://doi.org/10.1002/pmic.201600159. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.