Control Chart Pattern Recognition (CCPR) is essential for effective monitoring and fault detection in industrial processes. However, traditional manual interpretation methods face challenges such as vulnerability to noise and difficulty in capturing subtle variations in control chart patterns, limiting their reliability. This study aims to develop a robust automated CCPR system that enhances classification accuracy and reliability through a novel hybrid feature extraction approach combined with an adaptive weighted ensemble voting mechanism. The proposed approach comprises five main phases: Generate synthetic data with varying noise, hybrid feature extraction, feature selection, classifier model with adaptive weighted ensemble Voting - introduction of a dynamic weighting scheme that assigns confidence-based weights to each base classifier's prediction, enabling improved robustness and accuracy, especially under noisy conditions, and accuracy evaluation, the output of each phase is input for next phase. Experimental evaluation on 1,200 synthetic Shewhart chart samples covering six pattern types demonstrated that the proposed weighted ensemble classifier consistently outperformed individual models, achieving classification accuracies of up to 99.1% under noise-free conditions and maintaining high accuracy (98.3%) at realistic 10% noise levels. The ensemble also showed superior inference times and robustness, confirmed by strong confusion matrix diagonal dominance and low misclassification rates. This study presents a highly effective CCPR framework that combines rich hybrid features with an adaptive ensemble mechanism, significantly enhancing accuracy, interpretability, and suitability for real-time deployment. This work presents an adjective approach to developing industrial process monitoring systems that contribute to the early detection and resolution of faults, addressing the shortcomings of previous methods.