In software system development, it can be a challenge for people to select the ‘right’ requirement among several or many options if it is not obvious which requirement is desirable. Requirements prioritization helps people to discover the most desirable requirements. It seems that most requirements prioritization techniques work well on a small number of requirements, but many of them have constraints on medium to large numbers of requirements. This directly leads to a question: are there prioritization techniques that are suitable for people to prioritize medium to large numbers of requirements? In order to find an answer to this question, this research investigates the strength of evidence for the effectiveness of different requirements prioritization techniques for medium to large numbers of requirements. The methodology used for this research is a Systematic Literature Review. A Systematic Literature Review investigates research questions through identifying, evaluating and interpreting all relevant studies. It summarises the existing evidence for a certain technology. The reason a Systematic Literature Review was used to conduct this research is because it matches the purpose of this research, which is to systematically assess current studies in requirements prioritisation techniques as reported in literature, and analyse and draw together the results. After conducting the Systematic Literature Review, prioritization techniques that have been applied to medium to large numbers of requirements are identified and the strength of evidence for effectiveness of each technique is evaluated. It is found that the strength of evidence for effectiveness is weak for most prioritization techniques for large numbers of requirements. More studies on prioritization techniques for large numbers of requirements are needed. Stronger evidence presented for prioritization techniques for medium sized numbers of requirements shows the techniques are more mature. However, all the studies in the medium-size category use a subjective measure of improvement based on the users’ perceptions of level of improvement. It seems that the evaluations are still not strong for these studies.