<p>Depending on the nature of neurons, the output can be anything. The most popular neurons are linear, <a href="http://mathworld.wolfram.com/SigmoidFunction.html" rel="nofollow">sigmoidal curve</a> (range [0, 1]) and <a href="http://mathworld.wolfram.com/HyperbolicTangent.html" rel="nofollow">Hyperbolic Tangent</a> (range [-1, 1]). The first one can output any value. The latter two c approximate step function (i.e. binary behavior), but it is up to the end user (you) to define the cut-off value for that translation.</p>

<p>You didn't say which neurons you use, but you should definitely read more on how neural networks are implemented and how they work. You may start with <a href="http://www.youtube.com/watch?v=DG5-UyRBQD4" rel="nofollow">this video</a> and then read <a href="http://arxiv.org/ftp/cs/papers/0308/0308031.pdf" rel="nofollow">Artificial Neural Networks for Beginners </a> by C Gershenson.</p>

<p><strong>UPDATE</strong> You say that you use tanh-sigmoid neurons and wonder how come you don't get values either very close to -1 or to 1. </p>

<p>The output of tanh neuron is hyperbolic tangent of the sum of all its inputs. Every value between -1 and 1 is possible. What determines the "steepness" of the output (in other words: the proportion of interim values) is the output values of the preceding neurons and their weights. These depend on the output of their preceding neurons and their weights etc etc etc.  It is up to the learning algorithm to find the set of weights that minimizes a predefined scoring function, given a certain input. In a typical setup, a scoring function is a function that compares neural network output to a set of desired results and returns a single number that indicates how different the actual and the desired outputs are.</p>

<p>Before using NN you have to do some homework. At the minimum you have to decide what your goal is, how you interpret NN output and how you measure NN performance and how you update the weights. </p>
Why matlab neural network classification returns decimal values
<p>I have an input dataset (matrix 25x1575) which is normalized to values between 0 and 1. 
I also have a binary formatted output matrix (9x1575) like 0 0 0 0 0 0 0 0 1, 1 0 0 1 1 1 0 0 1 ... </p>

<p>I imported both files in matlab nntool and it automatically created a network with 25 input and 9 output nodes as I wanted.</p>

<p>After I trained this network using feed-forward backProp, I tested the model in its training data and each output nodes returns a decimal value like (-0.1978 0.45913 0.12748 0.25072 0.45199 0.59368 0.38359 0.31435 1.0604). </p>

<p>Why it doesn't return discrete values like 1 0 0 1 1 1 0 0 1? 
Is there any thing that I must set in nntool to get such values?</p>