Container Terminal is a specialized port facility for container vessels to load and unload shipping containers.  The movement of containers from the yard to the vessel for loading and from the vessel to the yard in an unloading operation is performed by yard cranes, terminal tractors and quay cranes. The quay cranes’ speed of handling of the containers is generally attributed towards the performance of a container terminal on a particular vessel, which is commonly indicated as Moves per Hour (MPH). As such, quay cranes with high reliability and availability are paramount to ensure uninterrupted operations so that the desired container handling performance is achieved. However, in many container terminals the reliability and availability of the quay cranes are impacted due to lack of maintenance, spare parts and procedures for timely refurbishment, retrofitting or replacement of the components or the quay cranes itself. This paper attempts to propose the appropriate maintenance activities to be carried out and the performance of the quay cranes are monitored, identify markers for impending obsolescence and propose the potential actions that can be taken to delay the onset of obsolescence such as refurbishment and retrofitting and its impact on the cost, reliability and availability and eventual replacement of the quay cranes. The approach is based on evaluation of breakdown frequency, breakdown hours, operating hours and container moves and most importantly the quay crane downtime and its impact on the overall vessel performance. In this study, the emphasis is on the performance and reliability of the electrical control system of the quay cranes. This analysis is based on 12 months of operational data. The correlation analysis for reliability has identified to have a good negative correlation (-0.75) with breakdown frequency. At the same time breakdown hours has strong correlation (0.93) with breakdown frequency. The 12 months data has identified the first step parameter for quay crane reliability prediction. The study is expected to explore 60 months of data in identifying more parameters to establish a quay crane reliability prediction model.