Edge detection is a previous step for image recognition systems that helps to extract the most important shapes in an image, ignoring the homogeneous regions and remarking the real object to classify or recognize. Traditional and fuzzy edge detectors can be used, but it's very difficult to demonstrate which one is better before the recognition results are obtained. In this work we present an experiment where several edge detectors were used to preprocess the same image sets. Each resultant image set was used as training data for a neural network recognition system, and the recognition rates were compared. The goal of this experiment is to find the better edge detector that can be used for the training data on a neural network to improve image recognition.