This paper presents classification techniques for analyzing mushroom dataset. Artificial Mushroom dataset is composed of records of different types of mushrooms, which are edible or non-edible. Aritificial Neural Network and Adaptive Nuero Fuzzy inference system are used for implementation of the classification techniques. Different techniques used for classification like ANN, ANFIS and Naïve Bayes are used to categorize different mushrooms as edible or non-edible. The performance of the different techniques is evaluated using accuracy, MAE, kappa statistic. After analyzing the results it was found that Adaptive Nuero Fuzzy inference System outperformed the other techniques with highest accuracy, lowest mean absolute error and ANN is the second best performer. If size of training set is increased, the accuracy also increased with respect to training set.