Detecting the shape of the non-rigid molten metal during welding, so-called weld pool visual sensing, is one of the central tasks for automating arc welding processes. It is challenging due to the strong interference of the high-intensity arc light and spatters as well as the lack of robust approaches to detect and represent the shape of the nonrigid weld pool. We propose a solution using active contours including an prior for the weld pool boundary composition. Also, we apply Adaboost to select a small set of features that captures the relevant information. The proposed method is applied to weld pool tracking and the presented results verified its feasibility. Note to Practitioners-Welding quality is highly dependent on the human welder's experiences and skills in the gas tungsten arc welding process. In particular, detecting the shape of the nonrigid molten metal during welding plays an important role in improving the welding quality since it provides a prime feedback mechanism for welding control. However, this is a challenging task due to a number of reasons, e.g., strong interference and changing working environment. Applying classical image processing algorithms directly, such as active contour models (ACM), cannot guarantee satisfactory performance due to the above-mentioned reasons. Actually, in practice, ACM more often than not failed to capture the welding pool boundary. In this work, we integrate an application-dependent prior to the definition of the energy functional of a traditional ACM, thus allowing us to use machine learning techniques to improve the performance of traditional ACM. In density estimation of the prior, AdaBoost is utilized to select features that are most suitable for Gaussian Bayesian classifiers to differentiate wanted boundaries and unwanted ones (e.g., spurious boundaries caused by interfering noise). The proposed approach improves the perfor-mance of the traditional ACM and captures the boundary of the welding pool successfully. It can also be generalized to other applications that need to integrate application-dependent priors to enhance classical methods, which may otherwise be not applicable in real-world implementations.