Understanding the deterioration and predominant damage mechanisms of structures is highly relevant, especially for safety-critical components. Non-Destructive Testing (NDT) plays a crucial role in assessing and monitoring their integrity by evaluating damage evolution. However, when it comes to complex structures, the existing NDT methods face challenges in their application, as is the case with sandwich structures. This study employs two NDT methods to analyze the initiation of damage in such structures during a fatigue test. The Acoustic Emission (AE) technique utilizes membrane-free microphones with a broad bandwidth to capture acoustic events from difficult-to-access areas. A machine learning algorithm is used to classify these events to determine their source and associated damage mechanisms. Additionally, Digital Image Correlation (DIC) is employed to measure strain evolution without contact during the test. This method is particularly relevant due to the complex and thin geometry of sandwich structures, where other methods are not applicable. Strain redistributions are considered relevant damage indicators. The results indicate that Acoustic Emission serves as an early indicator of damage, with the cumulative number of events and peak frequency correlating well with the severity of the damage. In contrast, DIC revealed clear indications of damage or deterioration, albeit at a later stage compared to AE.