The real life tasks to which artificial neural networks are applied fall within the following broad categories:
- Function approximation, or regression analysis, including time series prediction and modeling.
- Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
- Data processing, including filtering, clustering, blind source separation and compression.

Application areas include:
- System identification and control (vehicle control, process control),
- Game-playing and decision making (backgammon, chess, racing),
- Pattern recognition (radar systems, face identification, object recognition and more),
- Sequence recognition (gesture, speech, handwritten text recognition),
- Medical diagnosis,
- Financial applications,
- Data mining (or knowledge discovery in databases, "KDD"),
- Visualization and e-mail spam filtering.

The neural networks could find information about a person from:
- DMV records,
- Doctor's office records,
- Internet
- Other public sources

"What kind of patterns might the neural network show from sources like , my MD's office, the DMV?"
- From MD's office's records the neural network could inference the person's repetitive medical needs (illnesses)
- From DMV office's records the neural network could inference the person's driving habits (for instance, frequency of driving rules violations)