Data-driven methods are commonly used to identify abnormal operating conditions to maintain the health and safety of processes, which assume that the process data are precisely known and single-valued. However, in practice, process data are from multiple sources and are in various formats with uncertainties or measurement errors, which may lead to a high false-alarm rate and imprecise decisions. In addition, various key variables are difficult or impossible to measure online, and they are always estimated and described in terms of semantic information by operators or experts. In this work, a one-versus-rest interval radial basis function neural network (OVR-IRBF-NN)-based abnormality identification method is proposed for imprecise data and semantic information in the process industry. First, three types of process data, namely, precise single-valued variables (PSVVs), imprecise single-valued variables (ISVVs), and two-dimensional-interval-valued variables (2DIVVs), which are transformed from semantic information, are analyzed to investigate more comprehensive process information. Then, transformation approaches are presented for transforming these three types of data into one-dimensional intervals. Gaussian mixture model (GMM)-based measurement error estimation is developed for ISVVs, and an uncertain-unchanged interval adjustment strategy is proposed for 2DIVVs. Moreover, the one-versus-rest interval radial basis function neural network (OVR-IRBF-NN) is put forth for the identification of abnormal operating conditions via the analysis of one-dimensional-interval-valued data, providing more reliable and robust identification results due to the employment of interval-valued data. The proposed strategy is evaluated on both a numerical example and a real hydrometallurgical metallurgy leaching process, and the results demonstrate the feasibility and effectiveness of the strategy.