We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive inter-comparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer and visual observations were compared. Total cloud cover from the sky camera was in 65-85 % of cases within ± 1 octa with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 octas but underestimates it by 0.8 ± 1.9 octas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest-Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10 %. The performance of the algorithm mainly depends on the atmospheric conditions, site specific characteristics, the randomness of the selected images and possible visual misclassifications: The mean success rate was 80 - 90 % when the image only contained a single cloud class but dropped to 50 % if the test images were completely randomly selected and multiple cloud classes occurred in the images.