Flow visualization is an important topic in many scientific domains and has been an active field of research for many years. Many different methods of analysis can be used in order to analyze flow, however recently big progress have been reported on the manifold learning algorithms for high-dimensional data. This thesis investigates the use of Stochastic Neighbor Embedding (SNE) methods, t-distributed stochastic Neighbour embedding(t-SNE) and hierarchical stochastic Neighbor embedding (HSNE) for flow analysis. In this thesis the Manivault ,Veith et al., 2024, software platform has been used in order to create an interactive analysis tool for SNE methods used on flow data. This tool consists of a 3D viewer plugin that visualizes the full path lines and an existing scatterplot plugin that is used in order to interact with the created SNE maps. The experiments and comparisons reported in this thesis aimed to compare the use of t-SNE and HSNE for analysis of flow structures in 4D Flow MRI data. From this, it can be concluded both t-SNE and HSNE are useful for interaction with and analysis of 4D Flow MRI data. t-SNE can best be used in order to explore and analyze flow data in search for flow structures, and comparing flow patterns between subjects. HSNE on the other hand gives a better separation between different flow components, however at the expense of a less accurate preservation of vortices and other flow structures.