Filter design involves a trade-off between the size of the filter class over which optimization is to be performed and the size of the training sample. As the number of parameters determining the filter class grows, so too does the size of the training sample required to obtain a given degree of precision when estimating the optimal filter from the sample data. A common way to moderate the estimation problem is to use a constrained filter requiring less parameters, but then a trade-off between the theoretical filter performance and the estimation precision arises. The overall result strongly depends on the constraint type. Approaches presented in this paper divide the filter operation into two stages and apply constraints only to the first stage. Such filters are advantageous since they are fully optimal with respect to certain subsets of the filter window. Error expression, representation, and design methodology are discussed. A generic optimization algorithm for such two-stage filters is proposed. Special attention is paid to three particular cases, for which properties, design algorithms, and experimental results are provided: two-stage filters with linearly separable preprocessing, two-stage filters with restricted window preprocessing, and twostage iterative filters.