Harmonizable Process can be thought of as Fourier transforms of vector--valued measures. If the vector--valued measure has orthogonal increments, the harmonizable processes so obtained is stationary. Much of what is known about linear prediction and stationary processes has been generalized to harmonizable processes. This talk will review what has been done in this area. The classical moving average representation of stationary processes is generalized to moving average representations for discrete and continuous multidimensional strongly harmonizable processes. Necessary and sufficient conditions on covariance functions are given for the existence of such moving average representations. The study of strongly harmonizable processes is amiable to Fourier analytic methods and is of interest in applications such as prediction theory, filtering problems and others.