In industrial manufacturing, a variety of different materials are used to manufacture goods in a cost-efficient manner. In situations where multiple materials are being used, such as compound parts, the machining becomes particularly challenging, as various materials require the adaption of the respective machining process. A similar parameter adaption is necessary for the machining of material batches with varying machinability. However, the specific material batch or the exact transition point among multiple materials for compound parts might not be known. Thus, to enable material-specific parameter adaption for smart manufacturing systems, a material identification system is necessary. In this research, proposed material identification approaches are reviewed regarding the application scenario and the proposed solution. Based on the findings, a taxonomy is derived for classifying material identification tasks in one of the five categories: major material, material sub-class, specific material, material grade, and material batch. Analyzing the signals used for identification, it is shown that surface images, force data, and vibration data are most commonly used. Whereas images are often used as single modality, the remaining signals are typically paired in multi-modal sensing approaches. Focusing on the decision-making system, it can be seen that, especially for the time-series signals, extensive preprocessing and feature engineering is carried out to derive meaningful features from the raw signals. Consecutively, threshold systems or machine learning models are used for decision making. The most commonly investigated algorithms are artificial neural networks, support vector machines, and k-nearest-neighbor.