This paper presents a study for recognizing isolated Turkish handwritten uppercase letters. In the study, first of all, a Turkish Handprint Character Database has been created from the students in Istanbul Technical University (ITU). There are about 20000 uppercase and 7000 digit samples in this database. Several feature extraction and classification techniques are realized and combined to find the best recognition system for Turkish characters. Features, obtained from Karhunen-Loeve Transform, Zemike Moments, Angular Radial Transform and Geometric Features, are classified with Artificial Neural Networks, K-Nearest Neighbor, Nearest Mean, Bayes, Parzen and Size Dependent Negative Log-Likelihood methods. Geometric moments, which are suitable for Turkish characters, are formed. KLT features are fused with other features since KLT gives the best recognition rate but has no information about the shape of the character where other methods have. The fused features of KLT and ART classified by SDNLL gives the best result for Turkish characters in the experiments.