Control chart patterns (CCPs) recognition is an important issue in statistical process control, since abnormal CCPs exhibited in control charts can be associated with certain assignable causes and then help to quickly eliminate assignable causes of process variation. Most of the existing studies assume that the observed process data needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data may be concurrent CCPs which are mixed together by two basic CCPs. In this study, an integrated scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for recognizing concurrent CCPs. The proposed ICA-SVM scheme initially uses ICA with concurrent patterns for generating independent components (ICs). The hidden basic patterns of the concurrent patterns can be discovered in these ICs. The ICs are then used as input variables for the SVM for building a CCP recognition model. Experimental results reveal that the proposed ICA-SVM scheme can produce accurate and stable recognition results. It outperforms the three comparison models and is able to effectively recognize concurrent control chart patterns.