Relatively few studies have focused on systematically mining the patent databases of different countries. This study mines the databases of the main ‘technological powers’ using several methods. By using descriptive statistical methods, the study yields key insights regarding patenting activities affecting the succession and ‘crowding out’ of technologies, the ‘hottest technologies’ and the patent application strategies in these countries. The spectrums of technological strength in these countries are further analysed with Principal Component Analysis (PCA), as two principal components are sufficient to resolve over 92% of the total variance. The US, EU and China are the economies that all technological powers may regard as important; similarities in the application strategies used in these countries are thus further investigated. Another extensive analysis utilising K-means clustering is also performed. Except for the optimal number for patent clustering, surprisingly, the top 10 ‘most important technologies’ are identical to the top 10 hottest ones that were previously identified. The knowledge and insights gained from this study are valuable not only for technological development policy makers, but also for business decision makers seeking suitable markets and areas to enter and invest in. Some data visualization and analysis methods are applied for the first time to this knowledge discovery problem.