In this project, a predictive time model was developed for an Anglia Autoflow mechanical chicken catching system. At the completion of poultry growout, hand labor is currently used to collect the birds from the house, although some integrators are beginning to incorporate mechanical catching equipment. Several regression models were investigated with the objective of predicting the time taken to catch the chicken. A regression model relating distance to total time (sum of packing time, catching time, movement to catching and movement to packing) provided the best performance. The model was based on data collected from poultry farms on the Delmarva Peninsula during a six-month period. Statistical Analysis System (SAS) and NeuroShell Easy Predictor were used to build the regression and neural network models respectively. Model adequacy was established by both visual inspection and statistical techniques. The models were validated with experimental results not incorporated into the initial model.