Technical Abstract: Currently, sugarcane selection begins at the seedling stage with visual selection for cane yield and other yield-related traits. Although subjective and inefficient, visual selection remains the primary method for selection. Visual selection is inefficient because of the confounding effect of genotype by environment interaction and competition among closely spaced seedlings. Artificial Neural Network (ANN) models are mathematical models based on biological neural networks; they are a supervised learning method and use pattern learning from training data to produce predictions of response variables. In sugarcane, ANN models would use yield components, namely, stalk number, height, and diameter as predictor variables to produce a selection decision. The objective of this study was to examine the potential of using the SAS enterprise miner ANN models to identify seedlings with higher yield potential. Seedling data for stalk numbers, height, diameter, and the decision to either select or reject a seedling were collected from five crosses grown at the Louisiana State University Agricultural Center, Sugar Research Station, and 17 crosses grown at the United States Department of Agriculture, Sugar Cane Research Unit, in Louisiana, USA. The ANN models produced greater differences between the seedling cane yields of the selected and rejected seedlings than visual selection. At the same selection rate, seedlings selected with neural network models produced higher yields than those advanced by visual selection. The ANN models selected fewer seedlings with lower cane yield and rejected fewer seedlings with higher cane yield than the visual selection.