A primary aim in pharmacovigilance is the timely detection of either new adverse drug reactions (ADRs) or a relevant change of the frequency of ADRs that are already known to be associated with the drugs involved, i.e. signal detection. Adequate signal detection solely based on the human intellect (case-by-case analysis or qualitative signal detection) has proven its value. However, it is becoming increasingly time consuming given the growing volume of data, as well as less effective, especially in more complex associations, such as drug-drug interactions, syndromes and when various covariates are involved. In quantitative signal detection, measures that express the extent in which combinations of drug(s) and clinical event(s) are disproportionately present in the database of reported suspected ADRs are used to reveal associations of interest. Although the rationale and the methodology of the various quantitative approaches differ, they all share the characteristic in that they express to what extent the number of observed cases differs from the number of expected cases. Recent years have shown that the use of quantitative measures in addition to qualitative analysis is a step forward in signal detection in pharmacovigilance. This paper uses historical, classic examples and studies to illustrate the principles, pros and cons of especially quantitative methods in signal detection and adds a flavour of future perspective.