Pattern recognition is the automated recognition of patterns and regularities in data. It is a process that uses machine learning algorithms to classify input data into objects, classes, or categories based on recognized patterns, features, or regularities in data. Pattern recognition can be applied to various types of data, including text, images, sounds, and other definable qualities.

In machine learning, pattern recognition is the assignment of a label to a given input value. For example, classification is a type of pattern recognition that attempts to assign each input value to one of a given set of classes, such as determining whether a given email is "spam". Pattern recognition is a more general problem that encompasses other types of output as well.

In psychology and cognitive neuroscience, pattern recognition describes a cognitive process that matches information from a stimulus with information retrieved from memory). Spatial positioning in the environment, remembering findings, and detecting hazards and resources to increase chances of survival are examples of the application of pattern recognition for humans and animals).

Pattern recognition has a variety of applications, including image processing, speech and fingerprint recognition, aerial photo interpretation, optical character recognition in scanned documents such as contracts and photographs, and even medical imaging and diagnosis. It is also the technology behind data analytics and can be used to predict stock market outcomes.

In summary, pattern recognition is a process that uses machine learning algorithms to classify input data into objects, classes, or categories based on recognized patterns, features, or regularities in data. It has various applications in different fields, including machine learning, psychology, and data analytics.