Multistate operations are common in chemical plants and result in high-dimensional, multivariate, temporal data. In this two-part paper, we develop self-organizing map (SOM)-based approaches for visualizing and analyzing such data. In Part 1 of this paper, the SOM is used to reduce the dimensionality of the data and effectively visualize multistate operations in a three-dimensional map. A key characteristic of multistate processes is that the plant operates for long durations at steady states and undergoes brief transitions involving large changes in variable values. When classical SOM training algorithms are used on data from multistate processes, large portions of the SOM become dedicated to steady states. which exaggerates even minor noise in the data. Also, transitions are represented as discrete jumps on the SOM space, which makes it an ineffective tool for visualizing multistate operations. In this Part 1, we propose a new training strategy specifically targeted at multistate operations. In the proposed strategy, the training dataset is first resampled to yield equal representation of the different process states. The SOM is trained with this state-sampled dataset. Furthermore. clustering is applied to group neurons of high similarity into compact clusters. Through this strategy, modes and transitions of multistate operations are depicted differently, with process modes visualized as intuitive clusters and transitions as trajectories across the SOM. We illustrate the proposed strategy using two real-case studies, namely, startup of a laboratory-scale distillation unit and operation of a refinery hydrocracker.