A generic process visualization method is introduced, which visualizes real-time process information and correlations among variables on a 2D map using parametric t-SNE. As an unsupervised learning method, it learns the mapping by minimizing the Kullback-Leibler divergence between the original high-dimensional space and the latent space using a deep neural network. In practice, it is observed that the original parametric t-SNE method lacks generalization and struggles to visualize unseen operating conditions correctly. In this work, two steps to improve its generalization capacity are proposed. In the first step, the neural network is trained with additional dummy data, which is generated to mimic the possible unseen conditions. Additionally, the structure of the neural network is reformulated with a new activation function that was designed to improve generalization for process data. The capability of the proposed approach was tested on two case studies, the Tennessee Eastman Process (TEP) and an industrial pyrolysis reactor. The results indicate that the proposed approach outperforms conventional methods in visualization as well as generalization capacity for unseen process conditions.