A Kohonen Network, also known as a Self-Organizing Map (SOM), is a type of artificial neural network that helps in visualizing and clustering high-dimensional data. It works by organizing input data into a lower-dimensional space, typically a two-dimensional grid, where similar data points are grouped together. This makes it easier to identify patterns and relationships within complex datasets. The network consists of neurons that compete to become activated by input data. The winning neuron and its neighbors are adjusted to better represent the input, allowing the network to learn over time. This unsupervised learning approach is useful in various applications, such as image processing and data mining.