Directional drilling is an excellent option to extend the limited reservoir reach and contact offered by vertical wells. Pressure transient responses (PTR) of horizontal wells provide key information about the reservoirs drilled. In this study multilayer perceptron (MLP) neural networks are used to correctly identify reservoir models from pressure derivative curves derived from horizontal wells. To this end, 2560 pressure derivative curves for six distinct reservoir models are generated and used to design a machine-learning classifier. A single hidden layer MLP network with 5 neurons, trained with a scaled conjugate gradient algorithm, is selected as the best classifier. This smart classifier provides total classification accuracy of 98.3%, mean square error of 0.00725, and coefficient of determination of 0.97332 over the whole dataset. Performance accuracy of the proposed classifier is verified with real field data, synthetically generated noisy PTR, and some signals outside the range initially assessed by the training plus testing data subsets. The developed network can correctly identify the reservoir-flow model with a probability of close to 0.9. The novelty of this work is that it employs a large dataset of horizontal (not vertical) well tests applied to six reservoir-flow models and includes noisy data to train and verify a neural network model to reliably achieve a high-level of prediction accuracy.