9. The method as recited in claim 8 , wherein using a back propagation algorithm to train the neural network to recognize the specified pattern of input comprises: an act of submitting the plurality of samples to a corresponding plurality of input nodes of the neural network, the neural network having random link weights assigned from the input nodes to a plurality of hidden nodes and the neural network having random link weights assigned from the plurality of hidden nodes to a plurality of output nodes; an act of indicating to the neural network that the input gesture data is to cause the neural network to activate a specified output node to at least a specified threshold value, the specified output node assigned, from among the plurality of output nodes, for the specified symbol; an act of the neural network processing the plurality of samples based on the random link weights to activate values at each of the plurality of output nodes; an act of determining that the activated value at the specified output node is not at least the specified threshold value; an act of calculating an error value indicating the difference between the activated value at the specified output node and the specified threshold value; and an act of propagating the error value backwards from the plurality of output nodes into the neural network to adjust link weights within the neural network, the adjusted link weights causing the neural network to activate the specified output node closer to the specified threshold value when subsequently processing the plurality of samples.
8. The method as recited in claim 1 , wherein the act of encoding the input gesture data into an input vector of real values comprises taking a plurality of samples of each of the first direction movement data and the second direction movement data at a plurality of designated intervals between the beginning of the time period and the end of the time period.