A method for selecting regressors in nonlinear models with mixed discrete (categorical) and continuous inputs is proposed. Given a set of input-output data and an initial superset of potential inputs, the relevant inputs are selected by a model-free search algorithm. Fuzzy clustering is used to quantize continuous data into subsets that can be handled in a similar way as discrete data. Two simulation examples and one real-world data set are included to illustrate the performance of the proposed method and compare it with the performance of regression trees. For small to medium size problems (up to 15 candidate inputs), the proposed method works effectively. For larger problems, the computational load becomes too high.