Adaptive filters are known as powerful algorithms in statistical signal processing that are used in a wide range of signal processing applications such as channel equalization, noise cancellation, system modeling. Initially, the adaptive filters have been derived for real-value signals. The complex domain provides a natural processing framework for signals with intensity and direction components. Statistical signal processing in C has traditionally been viewed as a straightforward extension of the corresponding algorithms in the real domain R. However, recent developments in augmented complex statistics show that they do not make full use of the algebraic structure of the complex domain. For example, it was shown that the covariance matrix is not sufficient to model the statistics of noncircular signals and it is necessary to introduce the pseudocovariance matrix to fully capture the relation between the real and imaginary components of random vectors. It is also shown that the standard linear model is only sufficient for modeling proper signals, whereas an optimal model for 'improper' signals is provided by a widely linear model. Given these challenges, the comlex-valued adaptive signal processing and filtering opportunities arise. This special issue aims to provide a venue for ongoing research in novel complex domain adaptive filters, as well as new applications and performance analysis.