It is generally accepted that Technical Analysis offers a “qualitative” approach to financial trading: it relies on operators specific skill to capture significant patterns over the series of quotes, thus anticipating reversals, trends or lateral movements, hence trying to beat the market. Such technique has ever been variously considered, since it appears to come as a mixture of human sentiments and political/economical variables conditioning, rather than from solid scientific foundations, as in contrast with the Efficient Market Hypothesis (EMH), even in Simon weak form. However, the idea to get time series relevant features and use them to forecast future behavior of markets is quite remarkable. Starting from this evidence, I make use of a particular class of neural algorithms (Topology Representing Networks -TRNs) to introduce an artificial technical analysis, based upon capabilities of neural nets to catch out essential features of financial time series, simply by considering them as they are, that is data and nothing else. A competitive unsupervised algorithm is here in order: both a single net and sets of networks will be taken into account; all the movements (stay long, short, standby) are assumed to come exclusively as a result of neural pattern recognition, skipping any other possible consideration of technical, fundamental or political indicators. This choice has been supported by previous results, which have proved the ability of such kind of nets in recognizing low deterministic chaos in financial time series. Data from Dow Jones Industrial Average since 1915 up to now will be employed: in order to test the robustness of this approach, I will consider results over various epochs, each of them differing in that training and control set will be randomly sorted in turn.