Mixed Integer programming (MIP) is frequently used in agricultural economics to solve farm-level optimization problems, but it can be computationally intensive especially when the number of binary or integer variables becomes large. In order to speed up simulations, for instance for large-scale sensitivity analysis or application to larger farm populations, meta-models can be derived from the original MIP and applied as an approximator instead. To test and assess this approach, we train Artificial Neural Networks (ANNs) as a meta-model of a farm-scale MIP model. This study compares different ANNs from various perspectives to assess to what extent they are able to replace the original MIP model. Results show that ANNs are promising for meta-modeling as they are computationally efficient and can handle non-linear relationships, corner solutions, and jumpy behavior of the underlying farm optimization model.