Accurate detection of swallowing events is essential for objective assessment of swallowing function and long-term monitoring in daily living environments. In this study, we propose an automatic method for detecting swallowing segments from neck-mounted polyvinylidene fluoride (PVDF) film signals. The PVDF film simultaneously captures swallowing sounds, mechanomyography, and laryngeal elevation with high wearability, making it suitable for continuous monitoring outside clinical settings. First, multimodal signals were acquired during 17 predefined swallowing and non-swallowing tasks, and a decision tree classifier was trained using features extracted from the time and frequency domains. The trained classifier achieved an overall accuracy of approximately 48.6% for the 17-task classification. Based on the results of this multiclass classification, the tasks were further regrouped into swallowing and non-swallowing categories, resulting in a binary classification accuracy of 92.9%. The trained model was then applied to continuous signals recorded during natural eating conditions to detect swallowing segments. Evaluation against video-based annotations demonstrated a precision of 82.5%, a recall of 88.7%, and an F1-score of 85.5%. These results indicate that swallowing segments can be reasonably detected from PVDF-based multimodal signals even under natural eating conditions involving a mixture of swallowing and various non-swallowing movements. The proposed approach provides a practical foundation for unobtrusive swallowing monitoring in daily life and long-term care settings.