This chapter discusses a class of linear optimum discrete-time filters known as the Wiener filters. These filters are optimum in the sense of minimizing an appropriate function of the error, known as the cost function. This approach is common to all optimum filter designs. The cost function that is commonly used in filter design optimization is the mean-square error (MSE). Minimizing MSE involves only second-order statistics and leads to a theory of linear filtering that is useful in many practical applications. The chapter provides the examples which illustrate the use and utility of the Wiener filters. Filtering of noisy signals is extremely important, and the method has been used in many applications, such as speech in noisy environment, reception of data across a noisy channel and enhancement of images.