Specialty and fine chemicals are often manufactured in multipurpose batch production plants. Compared to continuous production, these plants offer increased flexibility at the cost of operational complexity. A recipe defines the sequence and process parameters of different batch phases that are needed to transform raw materials into the desired product. In some plants detailed information about the executed recipe is not always captured by data acquisition systems. Knowledge of these phases is essential for optimizing quality and throughput. State-of-the-art data-driven machine learning techniques can recognize recurrent patterns in noisy time series data, enabling automatic labeling of batch phases based on widely available sensor data. In this review paper, we provide an overview of several machine learning approaches that can be used in an industrial setting.