Crop diseases can affect yield and/or quality of the harvested commodity. This can influence profitability and raise the risks of farming. When the diseases are estimated early, the yield will increase by taking measures thanks to farmers. The rust disease is one of the most major crop diseases that affect crop yield. Rust disease can be defined as a fungus; it makes the crops weak by blocking food to the roots and leaves. It is named “rust” disease, since the spots on the leaves look like grain of rust which is coloured in the range of yellow to bright orange, to brown or red. Some spots have a planar surface, while others are raised. This disease is infectious amongst vegetations but not between flowers and vegetables. The rust firstly seems bright orange. Then, it turns to dark brown as it proceeds. The infected leaves drop off and the main stems will show diseased spots as it spreads. Finally, the crops will die (Dauber 2008). In general, rust disease can be found in three types of planting areas. These are yellow rust, brown rust and black rust. The most common type, called a leaf or brown rust. This disease is usually seen in the wet type long leaves. Another common type of rust disease in plants is called stripe or yellow rust. It is seen most frequently in the leaves. The last common type of rust is called black rust and which is the most destructive kind of rust disease and it causes about 50 % losses per month of crop production efficiency (Çoklu2011). In this paper, daylily leaf images are used as crop sample and derived from different agricultural sites under expert control and daylily rust disease is estimated by using GLCM based different classifier techniques. Before classification process, the features are extracted from images with using Gray Level CoOccurrence Matrix (GLCM) method and 7 parameters are derived by this method for each digital camera image. These parameters are contrast, correlation, energy, homogeneity, entropy, standard deviation and mean for first texture feature vector. Then, the extracted feature vectors are applied to different type of classifiers and these vectors are used as inputs in classification systems. The Multilayer Perceptron neural network (MLP) , kNearest Neighbor (k -NN) and Least Squares Support Vector Machine (LS-SVM) classifiers have been chosen for learning and testing of 53 image data where 32 of them belongs to class I (normal), 21 of them belongs to class II (rust diseased). Different structures of networks are tested and the results are compared in terms of testing performance for each network model. Artificial Neural Network (ANN) techniques are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In pattern recognition, the knearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. A Least Squares Support Vector Machine (LS-SVM) is a concept in computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis .These methods were used for classification system in this paper. Finally, the best performance was observed as 88.90% in the k-NN and MLP network with 7-5-1 structure. Our results suggest this method is an accurate and efficient means of estimating daylily rust disease.