Supervised learning techniques are applied to two meteorological satellite applications: cloud classification and tropical cyclone intensity estimation. Using a set of expertly labeled training data that consist of attribute records taken from the five spectral channels, a 1-nearest neighbor algorithm is employed to classify GOES-8 and GOES-10 data in terms of cloud type, clear sky, ground snow, etc. The cloud classification algorithm is currently running in real time with color-coded output images available for viewing on the web. Similarly, using a set of training data that consist of attribute records with corresponding best track intensity, a K-nearest neighbor algorithm is developed to estimate the intensity of tropical cylcones as seen in Special Sensor Microwave/Imager (SSM/I) data. Data from the 85 GHz (H-pol) channel and derived rain rate product are used to compute the attributes. Recent algorithm performance measures will be presented.