This work describes a methodology for plant classification based on the analysis of leaf textures by combining a multi-resolution technique, such as the two-dimensional (2D) Discrete Wavelet Transform (2D-DWT), statistical models and Gray-Level Co-occurrence Matrices (GLCM) in which some invariance (e.g. rotation and scale) are achieved. As a second step, an Artificial Neural Network (ANN) model is trained for automatic classifying plant species. The proposed approach was tested on the Flavia database. An overall classification accuracy of 91.85% was achieved which demonstrates that plants can be reliably classified using texture samples extracted from leaf tissues.