1. At a computer system including a multi-touch input display surface, a method for training the computer system to recognize input gesture data entered at the multi-touch input display surface as a corresponding specified input pattern, the method comprising: an act of accessing input gesture data for at least one example of a specified input pattern, the input gesture data representing detected contact on the multi-touch input display surface over a period of time, the input gesture data including for at least the one example of the specified input pattern: first direction movement data, the first direction movement data being a first calculated graph including a directional axis corresponding to a first axis and a time axis corresponding to the period of time contact was detected on the multi-touch input display surface, the first calculated graph indicating the position of detected contact on the multi-touch input display surface relative to the first axis over the time period; and second direction movement data, the second direction movement data being a second calculated graph including a directional axis corresponding to a second axis and a time axis corresponding to the period of time contact was detected on the multi-touch input display surface, the second calculated graph indicating the position of detected contact on the multi-touch input display surface relative to a second different axis over the time period; an act of encoding the input gesture data into an input vector of real values, each real value corresponding to a sample taken from the first or second axis, each real value corresponding to a time value from the period of time contact was detected on the multi-touch input display surface and including a directional axis value; an act of saving an indication that the real values contained in the input vector represent an example of the specified input pattern; an act of accessing a fully connected neural network, the neural network including input nodes, hidden nodes, and output nodes, the input nodes being fully connected to the hidden nodes and the hidden nodes being fully connected to the output nodes; an act of creating a training set to supply to the neural network to train the neural network to recognize the specified input pattern, the training set including at least the input vector and a corresponding output vector, the corresponding output vector indicating the desired output of the neural network that is to correspond to the specified input pattern; and an act of using a back propagation algorithm to train the neural network to recognize the specified pattern of input by training the neural network to generate output node values approximating the corresponding output vector in response to receiving the input vector.
12. The method as recited in claim 1 , wherein the act of saving an indication that the real values contained in the input vector represent an example of the specified input pattern comprise an act of saving an indication that the real values contained in the input vector represent an example of a user-defined input pattern.