Understanding customer usage space of engine manufacturers and industry OEMs poses challenges in terms of data noise, data variability and complex interrelations between characteristics of a specific customer duty cycle. Human interpretation of engine data limits the analytics to 2-3 dimensions at a time thus limiting our ability to understand trends in data only to 2-3 specific engine parameters simultaneously. Previous studies in this field have been limited to understanding trends in data for a single duty cycle based on mini trips and time domain segmented clustering analysis. The techniques have been used to determine representative cycles for specific applications. The study in this paper, discusses the use of K-Means Clustering algorithm to classify customer usage space into clusters based on similarities in higher dimensions for multiple duty cycles. The clusters are evaluated based on overall system scope and also for very specific sub-system/component- based on a day based unsegmented engine parameter values. Some particular applications of this methodology are discussed in the paper including – Generalized Clustering and Post Processing Visualization Techniques, Critical Customer Identification and Customer Representative Nominal Short-Cycle Generation – a moving window approach. Case studies with real customer data for various On - Highway commercial diesel applications are discussed for different sub systems to demonstrate the applications and power of the tool.