Technical analysis or charting aims on visually identifying geometrical patterns in price charts in order to anticipate price trends. This dissertation revisits the issue of technical analysis statistical validation, which has been tackled in the literature without taking care of the presence of heterogeneity and statistical dependence in the analyzed data - agglutinated return time series from many distinct securities. The main purpose of this study is to address the first cited problem by suggesting a methodology to homogenize the securities according to the probability distributions of their return series. The general steps of the methodology go through the identification of the data generating stochastic processes for the security returns, the clustering of similar securities and, finally, the analysis of the presence, or absence, of informational content coming from those price patterns. We illustrate the proposed methodology with several financial securities of the global market. Our investigation shows that there is a statistically significant informational content in two out of the three common patterns usually found through technical analysis, namely: triangles, rectangle and head & shoulders.