Purpose: There is the propensity of airline catering supply chains to adapt their performance measures in order to meet desired service level due to the challenges of the COVID-19 pandemic. The aim of this paper is to develop a set of metrics for airline catering organisations and explore the choices of SCOR based performance metrics during the COVID-19 pandemic. Design/methodology/approach: Case study approach involving an airline catering supply chain organisation is adopted in this research. The SCOR framework is applied in the context of the case study supply chain to develop a performance metrics model for the chain. The performance metrics model is analysed and validated by a set of experts in the case study organisation. The experts are formed into a focus group. The performance metrics were prioritised using MoSCoW method based on the experience of the focus group participants on COVID-19 challenges. Findings: A hierarchical performance measure framework is proposed, and a set of 55 metrics from the SCOR framework is identified and validated for airline catering supply chains. MoSCoW based prioritisation of the metrics by the focus group participants results in 7 Level-1 SCOR metrics and 13 Levels 2 & 3 SCOR metrics been considered as necessary to better mitigate COVID-19 pandemic challenges. Research limitations/implications: This research is based on a single case study and a small number of participants, which limits the generalisability of the results. Practical implications: With the development of performance metrics and the prioritisation presented in this paper, airline catering organisations can monitor their catering logistics performance and use techniques such as MoSCoW to decide performance measures priorities in situations such as the COVID-19 pandemic. Originality/value: The work contributes to measuring performance in the airline catering supply chain and the need for considering adapting performance metrics using techniques such as MoSCoW, during challenging periods as in the COVID-19 pandemic