Automated landmark detection may prove important for the examination and automatic analysis of real-time three-dimensional (3D) echocardiograms. By detecting 3D anatomical landmark points, the standard anatomical views can be extracted automatically in 3D ultrasound images of left ventricle, for better standardization and objective diagnosis. Furthermore, the landmarks can serve as an initialization for other analysis methods, such as segmentation. In this thesis we describe an algorithm that iteratively applies landmark detection in perpendicular planes of the 3D dataset. The landmark detection exploits a large database of expert annotated images, using an extensive set of Haar wavelet-like features for classification, resulting in fast detection times suitable for real-time applications. The detection is performed using two cascades of Adaboost classifiers, that work in different 2D planes, in a coarse to fine scheme. The method is evaluated by measuring the total detection error for the landmark points between the detected positions and the manual ones.