Decision-making in mine planning is characteristically multi-criteria in nature. Therefore, it requires multi-criteria decision analysis (MCDA) techniques to solve the decision-making problems. MCDA techniques are a subset of multi-criteria decision-making (MCDM) techniques. A bibliometric analysis revealed growth in the use of MCDA techniques in mine planning. This is evident in the increased publication frequency of mine-planning related case studies that have been solved using MCDA techniques, especially post the 1998 global economic meltdown and 2008 global financial crisis. A similar trend can be expected post the COVID-19 global pandemic experienced between 2020 and 2022. Decision-making using MCDA techniques involves several steps. Firstly, decision-making criteria and different feasible solutions (ie alternatives) relevant to the problem are identified. Secondly, weights are assigned to the identified criteria to indicate their relative importance. Thirdly, each alternative is scored against each criterion, and the resulting model is solved to rank the alternatives and identify the most preferred alternative. An important step in MCDA involves assigning weights to the criteria because if this step is done incorrectly, subsequent steps will yield an incorrect solution (ie wrong choice of best alternative). Despite the importance of assigning weights to criteria, not much research has been done on different methods of assigning weights to criteria. Therefore, this paper compared different methods used to assign criteria weights in MCDM and/or MCDA problems in mine planning. The findings from the bibliometric analysis indicated that six criteria weighting methods seem to be the preferred methods in MCDM and/or MCDA mine planning choice decisions. The comparison of the methods to determine criteria weights suggest that future research directions should include the use of hybrid methods and machine learning methods so that uncertainty inherent to MCDM and/or MCDA in mine planning cases can be accounted for and criteria weights determined more reliably.