diff --git a/lib/naive_bayes.js b/lib/naive_bayes.js
index a258db1..757e91e 100644
--- a/lib/naive_bayes.js
+++ b/lib/naive_bayes.js
@@ -177,13 +177,36 @@ Naivebayes.prototype.categorize = function (text) {
     , maxProbability = -Infinity
     , chosenCategory = null
 
+  var probabilities = self.probabilities(text)
+
+  //iterate thru our categories to find the one with max probability for this text
+  probabilities
+    .forEach(function (categoryProbability) {
+      if (categoryProbability.value > maxProbability) {
+        maxProbability = categoryProbability.value
+        chosenCategory = categoryProbability.category
+      }
+    })
+
+  return chosenCategory;
+}
+
+/**
+ * Determine category probabilities for `text`.
+ *
+ * @param  {String} text
+ * @return {Array} probabilities
+ */
+Naivebayes.prototype.probabilities = function(text) {
+  var self = this;
+
   var tokens = self.tokenizer(text)
   var frequencyTable = self.frequencyTable(tokens)
 
-  //iterate thru our categories to find the one with max probability for this text
-  Object
+  //iterate thru our categories to calculate the probability for this text
+  return Object
   .keys(self.categories)
-  .forEach(function (category) {
+  .map(function (category) {
 
     //start by calculating the overall probability of this category
     //=>  out of all documents we've ever looked at, how many were
@@ -206,13 +229,11 @@ Naivebayes.prototype.categorize = function (text) {
       logProbability += frequencyInText * Math.log(tokenProbability)
     })
 
-    if (logProbability > maxProbability) {
-      maxProbability = logProbability
-      chosenCategory = category
+    return {
+      category: category,
+      value: logProbability
     }
   })
-
-  return chosenCategory
 }
 
 /**
diff --git a/readme.md b/readme.md
index f268a5b..1daa933 100644
--- a/readme.md
+++ b/readme.md
@@ -70,6 +70,10 @@ Teach your classifier what `category` the `text` belongs to. The more you teach
 
 Returns the `category` it thinks `text` belongs to. Its judgement is based on what you have taught it with **.learn()**.
 
+###`classifier.probabilities(text)`
+
+Returns an array of `{ value, category }` objects with probability calculated for each category. Its judgement is based on what you have taught it with **.learn()**.
+
 ###`classifier.toJson()`
 
 Returns the JSON representation of a classifier.