Carbon Fiber Reinforced Polymers (CFRP) are increasingly used in aerospace and automotive applications due to their high strength-to-weight ratio, but their complex failure behavior unfortunately leads to significant challenges for structural health monitoring. The challenge of detecting and classifying damage events is additionally increased by simultaneously occurring damage events. Under load the occurrence of multiple damages at the same time is common, because the damage events can interact and trigger each other. The objective of this work is to distinguish parallel occurring damage mechanisms in CFRP using Acoustic Emission (AE). Specifically, the study aims to improve interpretability and classification accuracy of AE signals associated with overlapping damage events. Unsupervised clustering techniques combined with frequency- and time-frequency-domain feature extraction are applied to AE signals measured during controlled mechanical testing of CFRP laminates. The interdependence of damage events is investigated by analyzing the occurrence of simultaneously occurring damage events. The study reveals a distinct mapping of concurrent damage mechanisms to AE feature clusters under quasi-static and cyclic loading, demonstrating that coexisting failure modes can be individually identified. This finding marks an advancement in AE-based nondestructive evaluation, enabling more reliable condition monitoring of composite structures. The ability to distinguish simultaneous damages in CFRP allows predictive maintenance strategies and stimulates further development of smart monitoring systems for high-performance composites.