An intelligent transportation system (ITS) generally contains an automatic traffic video-surveillance system as a primary subsystem. Such subsystem incorporates the capabilities of neural networks for the efficient and effective recognition and classification of complicated spatial and temporal patterns in real-world traffic scenarios. Notably, the properties of input vectors are the key factors in determining the performance of neural networks. These properties are governed by the method used to normalize these vectors; a simple linear scaling model is widely employed for normalizing input vectors. This study proposes the use of a nonlinear normalization model for input vector normalization. The proposed technique is subsequently applied to neural networks to resolve classification problems encountered when analyzing real-world traffic image data. The experimental results show that the proposed model can produce higher prediction accuracy, when compared to the existing linear-based approach models. This model has the potential to improve the performance in traffic machine vision applications.