This work is part of an effort to develop of a knowledge–vision integration platform for hazard control in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment. The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting experiences from a new scenario, querying the user, and using their feedback to retrain the system through a hierarchical recognition structure using convolutional neural network (CNN). Thereafter, in contrast to the random sampling, the concept of active learning based on pruning of redundant points is tested. Results obtained from both random sampling and active learning are compared with a rigid systems that is not capable to aggregate new experiences as it runs. From the results obtained, it can be concluded that the classification accuracy improves greatly by adding new experiences, which makes it possible to customize the service according to each scenario and application as it functions. In addition, the active learning approach was able to reduce the user query and slightly improve the overall classification performance, when compared with random sampling.