This study investigates the feasibility of detecting drinking behavior in social environments using chest-mounted accelerometer data. A dataset collected during a conference is used, consisting of accelerometer data and annotated video recordings of 48 participants. After preprocessing, a random forest classifier is trained using statistical features: mean, correlation, maximum, minimum, and covariance derived from the yand z-axes of acceleration data. Evaluation through sixfold cross-validation for one participant yields an accuracy of 79%, while a leave-one-out cross-validation across participants achieves 70% accuracy. Qualitative analysis of false predictions reveals that actions like nodding, walking while drinking, or movement of the drinking hand towards the face can mimic drinking behavior. These findings demonstrate that accelerometer data contains detectable signals of drinking behavior even in noisy real-world conditions. However, further improvements require more diverse training data, consistent annotation, and possibly the inclusion of additional movement categories. The results support the potential of wearable accelerometers for drinking monitoring in social settings.