Accurate and speedy automatic recognition of Statistical Process Control Chart Patterns (SPCC) is a vital task for supervising manufacturing processes. This is done for better control to produce high-quality products. The motivation of this work is to increase the recognition accuracy of concurrent patterns. In this paper, a novel approach is proposed, using neural networks (NN) with Wavelet Analysis (WA) and Principal Component Analysis (PCA) to address the (CCP) recognition problem in concurrent patterns. Eight types of concurrent patterns based on a combination of normal patterns and unnatural patterns are addressed namely; stratification, systematic, increasing trend, decreasing trend, upshift, downshift, and cyclic. Thirteen statistical and shape features are proposed as inputs to the model. The main contribution of this work is the enhancement of the performance of NN through the augmentation of the signal (control chart data) using WA and proposing better extracted statistical features through the use of PCA. Our work shows that improving the original signal and using the right features improves the accuracy of the CCP recognition significantly. The proposed approach has an overall accuracy of 96.3%. The method was compared with four other methods from the previous literature, and it outperformed these methods.