Complex-valued neural networks (CvNN’s) continue to find their applications in various fields. The most common applications include wireless communications, computer vision, audio, image, radar, and sonar signal processing, cryptography, time series prediction, robotics, and soil moisture prediction, to name a few. CvNN’s have proven their superiority over real-valued neural networks (RvNN’s) in handling situations where both amplitude and phase information are needed. Such data naturally arises in many engineering applications. This chapter presents a brief, overview of CvNN’s, their applications, and associated challenges.